{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "ef430b55",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-12T01:05:43.630705Z",
     "start_time": "2024-09-12T01:05:43.612715Z"
    }
   },
   "outputs": [],
   "source": [
    "# To support both python 2 and python 3\n",
    "from __future__ import division, print_function, unicode_literals\n",
    "\n",
    "# Common imports\n",
    "import numpy as np\n",
    "import os\n",
    "\n",
    "# to make this notebook's output stable across runs\n",
    "np.random.seed(42)\n",
    "\n",
    "# To plot pretty figures\n",
    "%matplotlib inline \n",
    "#内嵌图片显示\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "mpl.rc('axes', labelsize=14)\n",
    "mpl.rc('xtick', labelsize=12)\n",
    "mpl.rc('ytick', labelsize=12)\n",
    "\n",
    "plt.rcParams['font.sans-serif'] = 'SimHei'\n",
    "plt.rcParams['axes.unicode_minus'] = False\n",
    "\n",
    "# Where to save the figures\n",
    "PROJECT_ROOT_DIR = \".\"\n",
    "CHAPTER_ID = \"fundamentals\"\n",
    "\n",
    "def save_fig(fig_id, tight_layout=True):\n",
    "    # path = os.path.join(PROJECT_ROOT_DIR, \"plt_images\", CHAPTER_ID, fig_id + \".png\")\n",
    "    path = os.path.join(PROJECT_ROOT_DIR, \"plt_images\", fig_id + \".png\")\n",
    "    print(\"Saving figure\", fig_id)\n",
    "    if tight_layout:\n",
    "        plt.tight_layout()\n",
    "    plt.savefig(path, format='png', dpi=300)\n",
    "\n",
    "# Ignore useless warnings (see SciPy issue #5998)\n",
    "import warnings\n",
    "warnings.filterwarnings(action=\"ignore\", message=\"^internal gelsd\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "b0648512",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-12T01:13:51.909408Z",
     "start_time": "2024-09-12T01:13:51.524457Z"
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "413baad7",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-12T01:24:15.562620Z",
     "start_time": "2024-09-12T01:24:15.551610Z"
    }
   },
   "outputs": [],
   "source": [
    "datapath = os.path.join(\"datasets\", \"\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "ddae97e3",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-12T01:28:34.882974Z",
     "start_time": "2024-09-12T01:28:34.858036Z"
    }
   },
   "outputs": [],
   "source": [
    "oecd_bli = pd.read_csv(datapath + 'oecd_bli_2015.csv', thousands=',')\n",
    "# gdp_per_capita = pd.read_excel(datapath + 'gdp_per_capita.csv', thousands=',')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "4b3f2139",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-12T01:29:07.312557Z",
     "start_time": "2024-09-12T01:29:07.273666Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 3292 entries, 0 to 3291\n",
      "Data columns (total 17 columns):\n",
      " #   Column                 Non-Null Count  Dtype  \n",
      "---  ------                 --------------  -----  \n",
      " 0   LOCATION               3292 non-null   object \n",
      " 1   Country                3292 non-null   object \n",
      " 2   INDICATOR              3292 non-null   object \n",
      " 3   Indicator              3292 non-null   object \n",
      " 4   MEASURE                3292 non-null   object \n",
      " 5   Measure                3292 non-null   object \n",
      " 6   INEQUALITY             3292 non-null   object \n",
      " 7   Inequality             3292 non-null   object \n",
      " 8   Unit Code              3292 non-null   object \n",
      " 9   Unit                   3292 non-null   object \n",
      " 10  PowerCode Code         3292 non-null   int64  \n",
      " 11  PowerCode              3292 non-null   object \n",
      " 12  Reference Period Code  0 non-null      float64\n",
      " 13  Reference Period       0 non-null      float64\n",
      " 14  Value                  3292 non-null   float64\n",
      " 15  Flag Codes             1120 non-null   object \n",
      " 16  Flags                  1120 non-null   object \n",
      "dtypes: float64(3), int64(1), object(13)\n",
      "memory usage: 437.3+ KB\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>LOCATION</th>\n",
       "      <th>Country</th>\n",
       "      <th>INDICATOR</th>\n",
       "      <th>Indicator</th>\n",
       "      <th>MEASURE</th>\n",
       "      <th>Measure</th>\n",
       "      <th>INEQUALITY</th>\n",
       "      <th>Inequality</th>\n",
       "      <th>Unit Code</th>\n",
       "      <th>Unit</th>\n",
       "      <th>PowerCode Code</th>\n",
       "      <th>PowerCode</th>\n",
       "      <th>Reference Period Code</th>\n",
       "      <th>Reference Period</th>\n",
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       "      <th>Flags</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AUS</td>\n",
       "      <td>Australia</td>\n",
       "      <td>HO_BASE</td>\n",
       "      <td>Dwellings without basic facilities</td>\n",
       "      <td>L</td>\n",
       "      <td>Value</td>\n",
       "      <td>TOT</td>\n",
       "      <td>Total</td>\n",
       "      <td>PC</td>\n",
       "      <td>Percentage</td>\n",
       "      <td>0</td>\n",
       "      <td>units</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.1</td>\n",
       "      <td>E</td>\n",
       "      <td>Estimated value</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AUT</td>\n",
       "      <td>Austria</td>\n",
       "      <td>HO_BASE</td>\n",
       "      <td>Dwellings without basic facilities</td>\n",
       "      <td>L</td>\n",
       "      <td>Value</td>\n",
       "      <td>TOT</td>\n",
       "      <td>Total</td>\n",
       "      <td>PC</td>\n",
       "      <td>Percentage</td>\n",
       "      <td>0</td>\n",
       "      <td>units</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>BEL</td>\n",
       "      <td>Belgium</td>\n",
       "      <td>HO_BASE</td>\n",
       "      <td>Dwellings without basic facilities</td>\n",
       "      <td>L</td>\n",
       "      <td>Value</td>\n",
       "      <td>TOT</td>\n",
       "      <td>Total</td>\n",
       "      <td>PC</td>\n",
       "      <td>Percentage</td>\n",
       "      <td>0</td>\n",
       "      <td>units</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CAN</td>\n",
       "      <td>Canada</td>\n",
       "      <td>HO_BASE</td>\n",
       "      <td>Dwellings without basic facilities</td>\n",
       "      <td>L</td>\n",
       "      <td>Value</td>\n",
       "      <td>TOT</td>\n",
       "      <td>Total</td>\n",
       "      <td>PC</td>\n",
       "      <td>Percentage</td>\n",
       "      <td>0</td>\n",
       "      <td>units</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CZE</td>\n",
       "      <td>Czech Republic</td>\n",
       "      <td>HO_BASE</td>\n",
       "      <td>Dwellings without basic facilities</td>\n",
       "      <td>L</td>\n",
       "      <td>Value</td>\n",
       "      <td>TOT</td>\n",
       "      <td>Total</td>\n",
       "      <td>PC</td>\n",
       "      <td>Percentage</td>\n",
       "      <td>0</td>\n",
       "      <td>units</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  LOCATION         Country INDICATOR                           Indicator  \\\n",
       "0      AUS       Australia   HO_BASE  Dwellings without basic facilities   \n",
       "1      AUT         Austria   HO_BASE  Dwellings without basic facilities   \n",
       "2      BEL         Belgium   HO_BASE  Dwellings without basic facilities   \n",
       "3      CAN          Canada   HO_BASE  Dwellings without basic facilities   \n",
       "4      CZE  Czech Republic   HO_BASE  Dwellings without basic facilities   \n",
       "\n",
       "  MEASURE Measure INEQUALITY Inequality Unit Code        Unit  PowerCode Code  \\\n",
       "0       L   Value        TOT      Total        PC  Percentage               0   \n",
       "1       L   Value        TOT      Total        PC  Percentage               0   \n",
       "2       L   Value        TOT      Total        PC  Percentage               0   \n",
       "3       L   Value        TOT      Total        PC  Percentage               0   \n",
       "4       L   Value        TOT      Total        PC  Percentage               0   \n",
       "\n",
       "  PowerCode  Reference Period Code  Reference Period  Value Flag Codes  \\\n",
       "0     units                    NaN               NaN    1.1          E   \n",
       "1     units                    NaN               NaN    1.0        NaN   \n",
       "2     units                    NaN               NaN    2.0        NaN   \n",
       "3     units                    NaN               NaN    0.2        NaN   \n",
       "4     units                    NaN               NaN    0.9        NaN   \n",
       "\n",
       "             Flags  \n",
       "0  Estimated value  \n",
       "1              NaN  \n",
       "2              NaN  \n",
       "3              NaN  \n",
       "4              NaN  "
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "oecd_bli.info()\n",
    "oecd_bli.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "921db5ed",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-12T01:32:04.592013Z",
     "start_time": "2024-09-12T01:32:04.585032Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "INEQUALITY\n",
       "TOT    888\n",
       "MN     881\n",
       "WMN    881\n",
       "HGH    328\n",
       "LW     314\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "oecd_bli['INEQUALITY'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "d456eb0b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-12T01:34:13.158673Z",
     "start_time": "2024-09-12T01:34:13.128754Z"
    }
   },
   "outputs": [
    {
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       "      <td>NaN</td>\n",
       "      <td>2.00</td>\n",
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       "      <th>3</th>\n",
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       "      <td>HO_BASE</td>\n",
       "      <td>Dwellings without basic facilities</td>\n",
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       "      <td>Value</td>\n",
       "      <td>TOT</td>\n",
       "      <td>Total</td>\n",
       "      <td>PC</td>\n",
       "      <td>Percentage</td>\n",
       "      <td>0</td>\n",
       "      <td>units</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CZE</td>\n",
       "      <td>Czech Republic</td>\n",
       "      <td>HO_BASE</td>\n",
       "      <td>Dwellings without basic facilities</td>\n",
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       "      <td>TOT</td>\n",
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       "      <td>0</td>\n",
       "      <td>units</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.90</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3213</th>\n",
       "      <td>EST</td>\n",
       "      <td>Estonia</td>\n",
       "      <td>WL_TNOW</td>\n",
       "      <td>Time devoted to leisure and personal care</td>\n",
       "      <td>L</td>\n",
       "      <td>Value</td>\n",
       "      <td>TOT</td>\n",
       "      <td>Total</td>\n",
       "      <td>HOUR</td>\n",
       "      <td>Hours</td>\n",
       "      <td>0</td>\n",
       "      <td>units</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14.90</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3214</th>\n",
       "      <td>ISR</td>\n",
       "      <td>Israel</td>\n",
       "      <td>WL_TNOW</td>\n",
       "      <td>Time devoted to leisure and personal care</td>\n",
       "      <td>L</td>\n",
       "      <td>Value</td>\n",
       "      <td>TOT</td>\n",
       "      <td>Total</td>\n",
       "      <td>HOUR</td>\n",
       "      <td>Hours</td>\n",
       "      <td>0</td>\n",
       "      <td>units</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14.48</td>\n",
       "      <td>E</td>\n",
       "      <td>Estimated value</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3215</th>\n",
       "      <td>RUS</td>\n",
       "      <td>Russia</td>\n",
       "      <td>WL_TNOW</td>\n",
       "      <td>Time devoted to leisure and personal care</td>\n",
       "      <td>L</td>\n",
       "      <td>Value</td>\n",
       "      <td>TOT</td>\n",
       "      <td>Total</td>\n",
       "      <td>HOUR</td>\n",
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       "      <td>0</td>\n",
       "      <td>units</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14.97</td>\n",
       "      <td>E</td>\n",
       "      <td>Estimated value</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3216</th>\n",
       "      <td>SVN</td>\n",
       "      <td>Slovenia</td>\n",
       "      <td>WL_TNOW</td>\n",
       "      <td>Time devoted to leisure and personal care</td>\n",
       "      <td>L</td>\n",
       "      <td>Value</td>\n",
       "      <td>TOT</td>\n",
       "      <td>Total</td>\n",
       "      <td>HOUR</td>\n",
       "      <td>Hours</td>\n",
       "      <td>0</td>\n",
       "      <td>units</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14.62</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3217</th>\n",
       "      <td>OECD</td>\n",
       "      <td>OECD - Total</td>\n",
       "      <td>WL_TNOW</td>\n",
       "      <td>Time devoted to leisure and personal care</td>\n",
       "      <td>L</td>\n",
       "      <td>Value</td>\n",
       "      <td>TOT</td>\n",
       "      <td>Total</td>\n",
       "      <td>HOUR</td>\n",
       "      <td>Hours</td>\n",
       "      <td>0</td>\n",
       "      <td>units</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14.97</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>888 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     LOCATION         Country INDICATOR  \\\n",
       "0         AUS       Australia   HO_BASE   \n",
       "1         AUT         Austria   HO_BASE   \n",
       "2         BEL         Belgium   HO_BASE   \n",
       "3         CAN          Canada   HO_BASE   \n",
       "4         CZE  Czech Republic   HO_BASE   \n",
       "...       ...             ...       ...   \n",
       "3213      EST         Estonia   WL_TNOW   \n",
       "3214      ISR          Israel   WL_TNOW   \n",
       "3215      RUS          Russia   WL_TNOW   \n",
       "3216      SVN        Slovenia   WL_TNOW   \n",
       "3217     OECD    OECD - Total   WL_TNOW   \n",
       "\n",
       "                                      Indicator MEASURE Measure INEQUALITY  \\\n",
       "0            Dwellings without basic facilities       L   Value        TOT   \n",
       "1            Dwellings without basic facilities       L   Value        TOT   \n",
       "2            Dwellings without basic facilities       L   Value        TOT   \n",
       "3            Dwellings without basic facilities       L   Value        TOT   \n",
       "4            Dwellings without basic facilities       L   Value        TOT   \n",
       "...                                         ...     ...     ...        ...   \n",
       "3213  Time devoted to leisure and personal care       L   Value        TOT   \n",
       "3214  Time devoted to leisure and personal care       L   Value        TOT   \n",
       "3215  Time devoted to leisure and personal care       L   Value        TOT   \n",
       "3216  Time devoted to leisure and personal care       L   Value        TOT   \n",
       "3217  Time devoted to leisure and personal care       L   Value        TOT   \n",
       "\n",
       "     Inequality Unit Code        Unit  PowerCode Code PowerCode  \\\n",
       "0         Total        PC  Percentage               0     units   \n",
       "1         Total        PC  Percentage               0     units   \n",
       "2         Total        PC  Percentage               0     units   \n",
       "3         Total        PC  Percentage               0     units   \n",
       "4         Total        PC  Percentage               0     units   \n",
       "...         ...       ...         ...             ...       ...   \n",
       "3213      Total      HOUR       Hours               0     units   \n",
       "3214      Total      HOUR       Hours               0     units   \n",
       "3215      Total      HOUR       Hours               0     units   \n",
       "3216      Total      HOUR       Hours               0     units   \n",
       "3217      Total      HOUR       Hours               0     units   \n",
       "\n",
       "      Reference Period Code  Reference Period  Value Flag Codes  \\\n",
       "0                       NaN               NaN   1.10          E   \n",
       "1                       NaN               NaN   1.00        NaN   \n",
       "2                       NaN               NaN   2.00        NaN   \n",
       "3                       NaN               NaN   0.20        NaN   \n",
       "4                       NaN               NaN   0.90        NaN   \n",
       "...                     ...               ...    ...        ...   \n",
       "3213                    NaN               NaN  14.90        NaN   \n",
       "3214                    NaN               NaN  14.48          E   \n",
       "3215                    NaN               NaN  14.97          E   \n",
       "3216                    NaN               NaN  14.62        NaN   \n",
       "3217                    NaN               NaN  14.97        NaN   \n",
       "\n",
       "                Flags  \n",
       "0     Estimated value  \n",
       "1                 NaN  \n",
       "2                 NaN  \n",
       "3                 NaN  \n",
       "4                 NaN  \n",
       "...               ...  \n",
       "3213              NaN  \n",
       "3214  Estimated value  \n",
       "3215  Estimated value  \n",
       "3216              NaN  \n",
       "3217              NaN  \n",
       "\n",
       "[888 rows x 17 columns]"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "oecd_bli = oecd_bli[oecd_bli['INEQUALITY'] == 'TOT']\n",
    "oecd_bli"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "e15bd1c1",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-12T01:40:39.812737Z",
     "start_time": "2024-09-12T01:40:39.793788Z"
    }
   },
   "outputs": [],
   "source": [
    "oecd_bli = oecd_bli.pivot(index='Country', columns='Indicator', values='Value')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "db1f58e3",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-12T01:40:42.209646Z",
     "start_time": "2024-09-12T01:40:42.194176Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Air pollution', 'Assault rate', 'Consultation on rule-making',\n",
       "       'Dwellings without basic facilities', 'Educational attainment',\n",
       "       'Employees working very long hours', 'Employment rate', 'Homicide rate',\n",
       "       'Household net adjusted disposable income',\n",
       "       'Household net financial wealth', 'Housing expenditure', 'Job security',\n",
       "       'Life expectancy', 'Life satisfaction', 'Long-term unemployment rate',\n",
       "       'Personal earnings', 'Quality of support network', 'Rooms per person',\n",
       "       'Self-reported health', 'Student skills',\n",
       "       'Time devoted to leisure and personal care', 'Voter turnout',\n",
       "       'Water quality', 'Years in education'],\n",
       "      dtype='object', name='Indicator')"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "oecd_bli.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "5f253d2f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-12T01:42:01.110611Z",
     "start_time": "2024-09-12T01:42:01.105624Z"
    }
   },
   "outputs": [],
   "source": [
    "oecd_bli.rename(columns={'Life satisfaction':'生活满意度'}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "44e2cbb4",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-12T01:42:17.842283Z",
     "start_time": "2024-09-12T01:42:17.826327Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Air pollution', 'Assault rate', 'Consultation on rule-making',\n",
       "       'Dwellings without basic facilities', 'Educational attainment',\n",
       "       'Employees working very long hours', 'Employment rate', 'Homicide rate',\n",
       "       'Household net adjusted disposable income',\n",
       "       'Household net financial wealth', 'Housing expenditure', 'Job security',\n",
       "       'Life expectancy', '生活满意度', 'Long-term unemployment rate',\n",
       "       'Personal earnings', 'Quality of support network', 'Rooms per person',\n",
       "       'Self-reported health', 'Student skills',\n",
       "       'Time devoted to leisure and personal care', 'Voter turnout',\n",
       "       'Water quality', 'Years in education'],\n",
       "      dtype='object', name='Indicator')"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "oecd_bli.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3d278aaa-256a-4756-a3e2-c6a5dd19bba5",
   "metadata": {},
   "source": [
    "默认情况pd.read.csv()假设字段之间由‘,’分隔，有些文件也可能使用其他字符(制表符\\t、分号;、空格等)作为字段分隔符；\n",
    "'latin1'是一种字符编码，能够表示西欧语言中绝大多数字符，但不包括中文等；\n",
    "默认下，pandas会将空字符串和numpy.nan视为缺失值。但是有些数据集会用(n/a、NA、None)来表示缺失值，可以通过na_values参数来将这些特定的字符串指定为缺失值。na_values=\"n/a\"会在读取csv文件将所有出现发“n/a”转换为NaN值。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "6c3eabbe",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-12T01:54:13.607279Z",
     "start_time": "2024-09-12T01:54:13.584613Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>Subject Descriptor</th>\n",
       "      <th>Units</th>\n",
       "      <th>Scale</th>\n",
       "      <th>Country/Series-specific Notes</th>\n",
       "      <th>2015</th>\n",
       "      <th>Estimates Start After</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Gross domestic product per capita, current prices</td>\n",
       "      <td>U.S. dollars</td>\n",
       "      <td>Units</td>\n",
       "      <td>See notes for:  Gross domestic product, curren...</td>\n",
       "      <td>599.994</td>\n",
       "      <td>2013.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albania</td>\n",
       "      <td>Gross domestic product per capita, current prices</td>\n",
       "      <td>U.S. dollars</td>\n",
       "      <td>Units</td>\n",
       "      <td>See notes for:  Gross domestic product, curren...</td>\n",
       "      <td>3995.383</td>\n",
       "      <td>2010.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>Gross domestic product per capita, current prices</td>\n",
       "      <td>U.S. dollars</td>\n",
       "      <td>Units</td>\n",
       "      <td>See notes for:  Gross domestic product, curren...</td>\n",
       "      <td>4318.135</td>\n",
       "      <td>2014.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Angola</td>\n",
       "      <td>Gross domestic product per capita, current prices</td>\n",
       "      <td>U.S. dollars</td>\n",
       "      <td>Units</td>\n",
       "      <td>See notes for:  Gross domestic product, curren...</td>\n",
       "      <td>4100.315</td>\n",
       "      <td>2014.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Antigua and Barbuda</td>\n",
       "      <td>Gross domestic product per capita, current prices</td>\n",
       "      <td>U.S. dollars</td>\n",
       "      <td>Units</td>\n",
       "      <td>See notes for:  Gross domestic product, curren...</td>\n",
       "      <td>14414.302</td>\n",
       "      <td>2011.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>185</th>\n",
       "      <td>Vietnam</td>\n",
       "      <td>Gross domestic product per capita, current prices</td>\n",
       "      <td>U.S. dollars</td>\n",
       "      <td>Units</td>\n",
       "      <td>See notes for:  Gross domestic product, curren...</td>\n",
       "      <td>2088.344</td>\n",
       "      <td>2012.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186</th>\n",
       "      <td>Yemen</td>\n",
       "      <td>Gross domestic product per capita, current prices</td>\n",
       "      <td>U.S. dollars</td>\n",
       "      <td>Units</td>\n",
       "      <td>See notes for:  Gross domestic product, curren...</td>\n",
       "      <td>1302.940</td>\n",
       "      <td>2008.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>187</th>\n",
       "      <td>Zambia</td>\n",
       "      <td>Gross domestic product per capita, current prices</td>\n",
       "      <td>U.S. dollars</td>\n",
       "      <td>Units</td>\n",
       "      <td>See notes for:  Gross domestic product, curren...</td>\n",
       "      <td>1350.151</td>\n",
       "      <td>2010.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>188</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>Gross domestic product per capita, current prices</td>\n",
       "      <td>U.S. dollars</td>\n",
       "      <td>Units</td>\n",
       "      <td>See notes for:  Gross domestic product, curren...</td>\n",
       "      <td>1064.350</td>\n",
       "      <td>2012.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>189</th>\n",
       "      <td>International Monetary Fund, World Economic Ou...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>190 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               Country  \\\n",
       "0                                          Afghanistan   \n",
       "1                                              Albania   \n",
       "2                                              Algeria   \n",
       "3                                               Angola   \n",
       "4                                  Antigua and Barbuda   \n",
       "..                                                 ...   \n",
       "185                                            Vietnam   \n",
       "186                                              Yemen   \n",
       "187                                             Zambia   \n",
       "188                                           Zimbabwe   \n",
       "189  International Monetary Fund, World Economic Ou...   \n",
       "\n",
       "                                    Subject Descriptor         Units  Scale  \\\n",
       "0    Gross domestic product per capita, current prices  U.S. dollars  Units   \n",
       "1    Gross domestic product per capita, current prices  U.S. dollars  Units   \n",
       "2    Gross domestic product per capita, current prices  U.S. dollars  Units   \n",
       "3    Gross domestic product per capita, current prices  U.S. dollars  Units   \n",
       "4    Gross domestic product per capita, current prices  U.S. dollars  Units   \n",
       "..                                                 ...           ...    ...   \n",
       "185  Gross domestic product per capita, current prices  U.S. dollars  Units   \n",
       "186  Gross domestic product per capita, current prices  U.S. dollars  Units   \n",
       "187  Gross domestic product per capita, current prices  U.S. dollars  Units   \n",
       "188  Gross domestic product per capita, current prices  U.S. dollars  Units   \n",
       "189                                                NaN           NaN    NaN   \n",
       "\n",
       "                         Country/Series-specific Notes       2015  \\\n",
       "0    See notes for:  Gross domestic product, curren...    599.994   \n",
       "1    See notes for:  Gross domestic product, curren...   3995.383   \n",
       "2    See notes for:  Gross domestic product, curren...   4318.135   \n",
       "3    See notes for:  Gross domestic product, curren...   4100.315   \n",
       "4    See notes for:  Gross domestic product, curren...  14414.302   \n",
       "..                                                 ...        ...   \n",
       "185  See notes for:  Gross domestic product, curren...   2088.344   \n",
       "186  See notes for:  Gross domestic product, curren...   1302.940   \n",
       "187  See notes for:  Gross domestic product, curren...   1350.151   \n",
       "188  See notes for:  Gross domestic product, curren...   1064.350   \n",
       "189                                                NaN        NaN   \n",
       "\n",
       "     Estimates Start After  \n",
       "0                   2013.0  \n",
       "1                   2010.0  \n",
       "2                   2014.0  \n",
       "3                   2014.0  \n",
       "4                   2011.0  \n",
       "..                     ...  \n",
       "185                 2012.0  \n",
       "186                 2008.0  \n",
       "187                 2010.0  \n",
       "188                 2012.0  \n",
       "189                    NaN  \n",
       "\n",
       "[190 rows x 7 columns]"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gdp_per_capita = pd.read_csv(datapath + 'gdp_per_capita.csv', thousands=',', delimiter='\\t',\n",
    "                            encoding='latin1', na_values=\"n/a\")\n",
    "gdp_per_capita"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "2ac0e970",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-12T01:55:54.428372Z",
     "start_time": "2024-09-12T01:55:54.414409Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "      <th>Units</th>\n",
       "      <th>Scale</th>\n",
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       "      <th>人均GDP</th>\n",
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       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Gross domestic product per capita, current prices</td>\n",
       "      <td>U.S. dollars</td>\n",
       "      <td>Units</td>\n",
       "      <td>See notes for:  Gross domestic product, curren...</td>\n",
       "      <td>599.994</td>\n",
       "      <td>2013.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albania</td>\n",
       "      <td>Gross domestic product per capita, current prices</td>\n",
       "      <td>U.S. dollars</td>\n",
       "      <td>Units</td>\n",
       "      <td>See notes for:  Gross domestic product, curren...</td>\n",
       "      <td>3995.383</td>\n",
       "      <td>2010.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>Gross domestic product per capita, current prices</td>\n",
       "      <td>U.S. dollars</td>\n",
       "      <td>Units</td>\n",
       "      <td>See notes for:  Gross domestic product, curren...</td>\n",
       "      <td>4318.135</td>\n",
       "      <td>2014.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Angola</td>\n",
       "      <td>Gross domestic product per capita, current prices</td>\n",
       "      <td>U.S. dollars</td>\n",
       "      <td>Units</td>\n",
       "      <td>See notes for:  Gross domestic product, curren...</td>\n",
       "      <td>4100.315</td>\n",
       "      <td>2014.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Antigua and Barbuda</td>\n",
       "      <td>Gross domestic product per capita, current prices</td>\n",
       "      <td>U.S. dollars</td>\n",
       "      <td>Units</td>\n",
       "      <td>See notes for:  Gross domestic product, curren...</td>\n",
       "      <td>14414.302</td>\n",
       "      <td>2011.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>185</th>\n",
       "      <td>Vietnam</td>\n",
       "      <td>Gross domestic product per capita, current prices</td>\n",
       "      <td>U.S. dollars</td>\n",
       "      <td>Units</td>\n",
       "      <td>See notes for:  Gross domestic product, curren...</td>\n",
       "      <td>2088.344</td>\n",
       "      <td>2012.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186</th>\n",
       "      <td>Yemen</td>\n",
       "      <td>Gross domestic product per capita, current prices</td>\n",
       "      <td>U.S. dollars</td>\n",
       "      <td>Units</td>\n",
       "      <td>See notes for:  Gross domestic product, curren...</td>\n",
       "      <td>1302.940</td>\n",
       "      <td>2008.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>187</th>\n",
       "      <td>Zambia</td>\n",
       "      <td>Gross domestic product per capita, current prices</td>\n",
       "      <td>U.S. dollars</td>\n",
       "      <td>Units</td>\n",
       "      <td>See notes for:  Gross domestic product, curren...</td>\n",
       "      <td>1350.151</td>\n",
       "      <td>2010.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>188</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>Gross domestic product per capita, current prices</td>\n",
       "      <td>U.S. dollars</td>\n",
       "      <td>Units</td>\n",
       "      <td>See notes for:  Gross domestic product, curren...</td>\n",
       "      <td>1064.350</td>\n",
       "      <td>2012.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>189</th>\n",
       "      <td>International Monetary Fund, World Economic Ou...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>190 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               Country  \\\n",
       "0                                          Afghanistan   \n",
       "1                                              Albania   \n",
       "2                                              Algeria   \n",
       "3                                               Angola   \n",
       "4                                  Antigua and Barbuda   \n",
       "..                                                 ...   \n",
       "185                                            Vietnam   \n",
       "186                                              Yemen   \n",
       "187                                             Zambia   \n",
       "188                                           Zimbabwe   \n",
       "189  International Monetary Fund, World Economic Ou...   \n",
       "\n",
       "                                    Subject Descriptor         Units  Scale  \\\n",
       "0    Gross domestic product per capita, current prices  U.S. dollars  Units   \n",
       "1    Gross domestic product per capita, current prices  U.S. dollars  Units   \n",
       "2    Gross domestic product per capita, current prices  U.S. dollars  Units   \n",
       "3    Gross domestic product per capita, current prices  U.S. dollars  Units   \n",
       "4    Gross domestic product per capita, current prices  U.S. dollars  Units   \n",
       "..                                                 ...           ...    ...   \n",
       "185  Gross domestic product per capita, current prices  U.S. dollars  Units   \n",
       "186  Gross domestic product per capita, current prices  U.S. dollars  Units   \n",
       "187  Gross domestic product per capita, current prices  U.S. dollars  Units   \n",
       "188  Gross domestic product per capita, current prices  U.S. dollars  Units   \n",
       "189                                                NaN           NaN    NaN   \n",
       "\n",
       "                         Country/Series-specific Notes      人均GDP  \\\n",
       "0    See notes for:  Gross domestic product, curren...    599.994   \n",
       "1    See notes for:  Gross domestic product, curren...   3995.383   \n",
       "2    See notes for:  Gross domestic product, curren...   4318.135   \n",
       "3    See notes for:  Gross domestic product, curren...   4100.315   \n",
       "4    See notes for:  Gross domestic product, curren...  14414.302   \n",
       "..                                                 ...        ...   \n",
       "185  See notes for:  Gross domestic product, curren...   2088.344   \n",
       "186  See notes for:  Gross domestic product, curren...   1302.940   \n",
       "187  See notes for:  Gross domestic product, curren...   1350.151   \n",
       "188  See notes for:  Gross domestic product, curren...   1064.350   \n",
       "189                                                NaN        NaN   \n",
       "\n",
       "     Estimates Start After  \n",
       "0                   2013.0  \n",
       "1                   2010.0  \n",
       "2                   2014.0  \n",
       "3                   2014.0  \n",
       "4                   2011.0  \n",
       "..                     ...  \n",
       "185                 2012.0  \n",
       "186                 2008.0  \n",
       "187                 2010.0  \n",
       "188                 2012.0  \n",
       "189                    NaN  \n",
       "\n",
       "[190 rows x 7 columns]"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gdp_per_capita.rename(columns={'2015':'人均GDP'}, inplace=True)\n",
    "gdp_per_capita"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e91ad2f3-3e05-45eb-ae62-c33c7f64d632",
   "metadata": {},
   "source": [
    "把‘Country’作为行索引"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "56cc20b6-7d27-45be-97de-3ec79a12c936",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Afghanistan', 'Albania', 'Algeria', 'Angola',\n",
       "       'Antigua and Barbuda', 'Argentina', 'Armenia', 'Australia',\n",
       "       'Austria', 'Azerbaijan', 'The Bahamas', 'Bahrain', 'Bangladesh',\n",
       "       'Barbados', 'Belarus', 'Belgium', 'Belize', 'Benin', 'Bhutan',\n",
       "       'Bolivia', 'Bosnia and Herzegovina', 'Botswana', 'Brazil',\n",
       "       'Brunei Darussalam', 'Bulgaria', 'Burkina Faso', 'Burundi',\n",
       "       'Cabo Verde', 'Cambodia', 'Cameroon', 'Canada',\n",
       "       'Central African Republic', 'Chad', 'Chile', 'China', 'Colombia',\n",
       "       'Comoros', 'Democratic Republic of the Congo', 'Republic of Congo',\n",
       "       'Costa Rica', \"Côte d'Ivoire\", 'Croatia', 'Cyprus',\n",
       "       'Czech Republic', 'Denmark', 'Djibouti', 'Dominica',\n",
       "       'Dominican Republic', 'Ecuador', 'Egypt', 'El Salvador',\n",
       "       'Equatorial Guinea', 'Eritrea', 'Estonia', 'Ethiopia', 'Fiji',\n",
       "       'Finland', 'France', 'Gabon', 'The Gambia', 'Georgia', 'Germany',\n",
       "       'Ghana', 'Greece', 'Grenada', 'Guatemala', 'Guinea',\n",
       "       'Guinea-Bissau', 'Guyana', 'Haiti', 'Honduras', 'Hong Kong SAR',\n",
       "       'Hungary', 'Iceland', 'India', 'Indonesia',\n",
       "       'Islamic Republic of Iran', 'Iraq', 'Ireland', 'Israel', 'Italy',\n",
       "       'Jamaica', 'Japan', 'Jordan', 'Kazakhstan', 'Kenya', 'Kiribati',\n",
       "       'Korea', 'Kosovo', 'Kuwait', 'Kyrgyz Republic', 'Lao P.D.R.',\n",
       "       'Latvia', 'Lebanon', 'Lesotho', 'Liberia', 'Libya', 'Lithuania',\n",
       "       'Luxembourg', 'FYR Macedonia', 'Madagascar', 'Malawi', 'Malaysia',\n",
       "       'Maldives', 'Mali', 'Malta', 'Marshall Islands', 'Mauritania',\n",
       "       'Mauritius', 'Mexico', 'Micronesia', 'Moldova', 'Mongolia',\n",
       "       'Montenegro', 'Morocco', 'Mozambique', 'Myanmar', 'Namibia',\n",
       "       'Nepal', 'Netherlands', 'New Zealand', 'Nicaragua', 'Niger',\n",
       "       'Nigeria', 'Norway', 'Oman', 'Pakistan', 'Palau', 'Panama',\n",
       "       'Papua New Guinea', 'Paraguay', 'Peru', 'Philippines', 'Poland',\n",
       "       'Portugal', 'Qatar', 'Romania', 'Russia', 'Rwanda', 'Samoa',\n",
       "       'San Marino', 'São Tomé and Príncipe', 'Saudi Arabia', 'Senegal',\n",
       "       'Serbia', 'Seychelles', 'Sierra Leone', 'Singapore',\n",
       "       'Slovak Republic', 'Slovenia', 'Solomon Islands', 'South Africa',\n",
       "       'South Sudan', 'Spain', 'Sri Lanka', 'St. Kitts and Nevis',\n",
       "       'St. Lucia', 'St. Vincent and the Grenadines', 'Sudan', 'Suriname',\n",
       "       'Swaziland', 'Sweden', 'Switzerland', 'Syria',\n",
       "       'Taiwan Province of China', 'Tajikistan', 'Tanzania', 'Thailand',\n",
       "       'Timor-Leste', 'Togo', 'Tonga', 'Trinidad and Tobago', 'Tunisia',\n",
       "       'Turkey', 'Turkmenistan', 'Tuvalu', 'Uganda', 'Ukraine',\n",
       "       'United Arab Emirates', 'United Kingdom', 'United States',\n",
       "       'Uruguay', 'Uzbekistan', 'Vanuatu', 'Venezuela', 'Vietnam',\n",
       "       'Yemen', 'Zambia', 'Zimbabwe',\n",
       "       'International Monetary Fund, World Economic Outlook Database, April 2016'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gdp_per_capita['Country'].unique() #获取所有的国家"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "21758fb8-beae-4910-a790-f7a058155101",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Subject Descriptor</th>\n",
       "      <th>Units</th>\n",
       "      <th>Scale</th>\n",
       "      <th>Country/Series-specific Notes</th>\n",
       "      <th>人均GDP</th>\n",
       "      <th>Estimates Start After</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Afghanistan</th>\n",
       "      <td>Gross domestic product per capita, current prices</td>\n",
       "      <td>U.S. dollars</td>\n",
       "      <td>Units</td>\n",
       "      <td>See notes for:  Gross domestic product, curren...</td>\n",
       "      <td>599.994</td>\n",
       "      <td>2013.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Albania</th>\n",
       "      <td>Gross domestic product per capita, current prices</td>\n",
       "      <td>U.S. dollars</td>\n",
       "      <td>Units</td>\n",
       "      <td>See notes for:  Gross domestic product, curren...</td>\n",
       "      <td>3995.383</td>\n",
       "      <td>2010.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Algeria</th>\n",
       "      <td>Gross domestic product per capita, current prices</td>\n",
       "      <td>U.S. dollars</td>\n",
       "      <td>Units</td>\n",
       "      <td>See notes for:  Gross domestic product, curren...</td>\n",
       "      <td>4318.135</td>\n",
       "      <td>2014.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Angola</th>\n",
       "      <td>Gross domestic product per capita, current prices</td>\n",
       "      <td>U.S. dollars</td>\n",
       "      <td>Units</td>\n",
       "      <td>See notes for:  Gross domestic product, curren...</td>\n",
       "      <td>4100.315</td>\n",
       "      <td>2014.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Antigua and Barbuda</th>\n",
       "      <td>Gross domestic product per capita, current prices</td>\n",
       "      <td>U.S. dollars</td>\n",
       "      <td>Units</td>\n",
       "      <td>See notes for:  Gross domestic product, curren...</td>\n",
       "      <td>14414.302</td>\n",
       "      <td>2011.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vietnam</th>\n",
       "      <td>Gross domestic product per capita, current prices</td>\n",
       "      <td>U.S. dollars</td>\n",
       "      <td>Units</td>\n",
       "      <td>See notes for:  Gross domestic product, curren...</td>\n",
       "      <td>2088.344</td>\n",
       "      <td>2012.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yemen</th>\n",
       "      <td>Gross domestic product per capita, current prices</td>\n",
       "      <td>U.S. dollars</td>\n",
       "      <td>Units</td>\n",
       "      <td>See notes for:  Gross domestic product, curren...</td>\n",
       "      <td>1302.940</td>\n",
       "      <td>2008.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zambia</th>\n",
       "      <td>Gross domestic product per capita, current prices</td>\n",
       "      <td>U.S. dollars</td>\n",
       "      <td>Units</td>\n",
       "      <td>See notes for:  Gross domestic product, curren...</td>\n",
       "      <td>1350.151</td>\n",
       "      <td>2010.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zimbabwe</th>\n",
       "      <td>Gross domestic product per capita, current prices</td>\n",
       "      <td>U.S. dollars</td>\n",
       "      <td>Units</td>\n",
       "      <td>See notes for:  Gross domestic product, curren...</td>\n",
       "      <td>1064.350</td>\n",
       "      <td>2012.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>International Monetary Fund, World Economic Outlook Database, April 2016</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>190 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                                   Subject Descriptor  \\\n",
       "Country                                                                                                 \n",
       "Afghanistan                                         Gross domestic product per capita, current prices   \n",
       "Albania                                             Gross domestic product per capita, current prices   \n",
       "Algeria                                             Gross domestic product per capita, current prices   \n",
       "Angola                                              Gross domestic product per capita, current prices   \n",
       "Antigua and Barbuda                                 Gross domestic product per capita, current prices   \n",
       "...                                                                                               ...   \n",
       "Vietnam                                             Gross domestic product per capita, current prices   \n",
       "Yemen                                               Gross domestic product per capita, current prices   \n",
       "Zambia                                              Gross domestic product per capita, current prices   \n",
       "Zimbabwe                                            Gross domestic product per capita, current prices   \n",
       "International Monetary Fund, World Economic Out...                                                NaN   \n",
       "\n",
       "                                                           Units  Scale  \\\n",
       "Country                                                                   \n",
       "Afghanistan                                         U.S. dollars  Units   \n",
       "Albania                                             U.S. dollars  Units   \n",
       "Algeria                                             U.S. dollars  Units   \n",
       "Angola                                              U.S. dollars  Units   \n",
       "Antigua and Barbuda                                 U.S. dollars  Units   \n",
       "...                                                          ...    ...   \n",
       "Vietnam                                             U.S. dollars  Units   \n",
       "Yemen                                               U.S. dollars  Units   \n",
       "Zambia                                              U.S. dollars  Units   \n",
       "Zimbabwe                                            U.S. dollars  Units   \n",
       "International Monetary Fund, World Economic Out...           NaN    NaN   \n",
       "\n",
       "                                                                        Country/Series-specific Notes  \\\n",
       "Country                                                                                                 \n",
       "Afghanistan                                         See notes for:  Gross domestic product, curren...   \n",
       "Albania                                             See notes for:  Gross domestic product, curren...   \n",
       "Algeria                                             See notes for:  Gross domestic product, curren...   \n",
       "Angola                                              See notes for:  Gross domestic product, curren...   \n",
       "Antigua and Barbuda                                 See notes for:  Gross domestic product, curren...   \n",
       "...                                                                                               ...   \n",
       "Vietnam                                             See notes for:  Gross domestic product, curren...   \n",
       "Yemen                                               See notes for:  Gross domestic product, curren...   \n",
       "Zambia                                              See notes for:  Gross domestic product, curren...   \n",
       "Zimbabwe                                            See notes for:  Gross domestic product, curren...   \n",
       "International Monetary Fund, World Economic Out...                                                NaN   \n",
       "\n",
       "                                                        人均GDP  \\\n",
       "Country                                                         \n",
       "Afghanistan                                           599.994   \n",
       "Albania                                              3995.383   \n",
       "Algeria                                              4318.135   \n",
       "Angola                                               4100.315   \n",
       "Antigua and Barbuda                                 14414.302   \n",
       "...                                                       ...   \n",
       "Vietnam                                              2088.344   \n",
       "Yemen                                                1302.940   \n",
       "Zambia                                               1350.151   \n",
       "Zimbabwe                                             1064.350   \n",
       "International Monetary Fund, World Economic Out...        NaN   \n",
       "\n",
       "                                                    Estimates Start After  \n",
       "Country                                                                    \n",
       "Afghanistan                                                        2013.0  \n",
       "Albania                                                            2010.0  \n",
       "Algeria                                                            2014.0  \n",
       "Angola                                                             2014.0  \n",
       "Antigua and Barbuda                                                2011.0  \n",
       "...                                                                   ...  \n",
       "Vietnam                                                            2012.0  \n",
       "Yemen                                                              2008.0  \n",
       "Zambia                                                             2010.0  \n",
       "Zimbabwe                                                           2012.0  \n",
       "International Monetary Fund, World Economic Out...                    NaN  \n",
       "\n",
       "[190 rows x 6 columns]"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#修改行索引为‘Country’\n",
    "gdp_per_capita.set_index(\"Country\", inplace=True)\n",
    "gdp_per_capita"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6c646baf-3fc6-464b-9cfd-d751d1d44dd3",
   "metadata": {},
   "source": [
    "对数据的预处理完毕<br>\n",
    "接下来合并数据作为训练模型的数据集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "78d4efea-1112-4822-b400-a8fb12178a43",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Air pollution</th>\n",
       "      <th>Assault rate</th>\n",
       "      <th>Consultation on rule-making</th>\n",
       "      <th>Dwellings without basic facilities</th>\n",
       "      <th>Educational attainment</th>\n",
       "      <th>Employees working very long hours</th>\n",
       "      <th>Employment rate</th>\n",
       "      <th>Homicide rate</th>\n",
       "      <th>Household net adjusted disposable income</th>\n",
       "      <th>Household net financial wealth</th>\n",
       "      <th>...</th>\n",
       "      <th>Time devoted to leisure and personal care</th>\n",
       "      <th>Voter turnout</th>\n",
       "      <th>Water quality</th>\n",
       "      <th>Years in education</th>\n",
       "      <th>Subject Descriptor</th>\n",
       "      <th>Units</th>\n",
       "      <th>Scale</th>\n",
       "      <th>Country/Series-specific Notes</th>\n",
       "      <th>人均GDP</th>\n",
       "      <th>Estimates Start After</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country</th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Brazil</th>\n",
       "      <td>18.0</td>\n",
       "      <td>7.9</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.7</td>\n",
       "      <td>45.0</td>\n",
       "      <td>10.41</td>\n",
       "      <td>67.0</td>\n",
       "      <td>25.5</td>\n",
       "      <td>11664.0</td>\n",
       "      <td>6844.0</td>\n",
       "      <td>...</td>\n",
       "      <td>14.97</td>\n",
       "      <td>79.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>16.3</td>\n",
       "      <td>Gross domestic product per capita, current prices</td>\n",
       "      <td>U.S. dollars</td>\n",
       "      <td>Units</td>\n",
       "      <td>See notes for:  Gross domestic product, curren...</td>\n",
       "      <td>8669.998</td>\n",
       "      <td>2014.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mexico</th>\n",
       "      <td>30.0</td>\n",
       "      <td>12.8</td>\n",
       "      <td>9.0</td>\n",
       "      <td>4.2</td>\n",
       "      <td>37.0</td>\n",
       "      <td>28.83</td>\n",
       "      <td>61.0</td>\n",
       "      <td>23.4</td>\n",
       "      <td>13085.0</td>\n",
       "      <td>9056.0</td>\n",
       "      <td>...</td>\n",
       "      <td>13.89</td>\n",
       "      <td>63.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>14.4</td>\n",
       "      <td>Gross domestic product per capita, current prices</td>\n",
       "      <td>U.S. dollars</td>\n",
       "      <td>Units</td>\n",
       "      <td>See notes for:  Gross domestic product, curren...</td>\n",
       "      <td>9009.280</td>\n",
       "      <td>2015.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Russia</th>\n",
       "      <td>15.0</td>\n",
       "      <td>3.8</td>\n",
       "      <td>2.5</td>\n",
       "      <td>15.1</td>\n",
       "      <td>94.0</td>\n",
       "      <td>0.16</td>\n",
       "      <td>69.0</td>\n",
       "      <td>12.8</td>\n",
       "      <td>19292.0</td>\n",
       "      <td>3412.0</td>\n",
       "      <td>...</td>\n",
       "      <td>14.97</td>\n",
       "      <td>65.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>Gross domestic product per capita, current prices</td>\n",
       "      <td>U.S. dollars</td>\n",
       "      <td>Units</td>\n",
       "      <td>See notes for:  Gross domestic product, curren...</td>\n",
       "      <td>9054.914</td>\n",
       "      <td>2015.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Turkey</th>\n",
       "      <td>35.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.5</td>\n",
       "      <td>12.7</td>\n",
       "      <td>34.0</td>\n",
       "      <td>40.86</td>\n",
       "      <td>50.0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>14095.0</td>\n",
       "      <td>3251.0</td>\n",
       "      <td>...</td>\n",
       "      <td>13.42</td>\n",
       "      <td>88.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>16.4</td>\n",
       "      <td>Gross domestic product per capita, current prices</td>\n",
       "      <td>U.S. dollars</td>\n",
       "      <td>Units</td>\n",
       "      <td>See notes for:  Gross domestic product, curren...</td>\n",
       "      <td>9437.372</td>\n",
       "      <td>2013.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hungary</th>\n",
       "      <td>15.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>7.9</td>\n",
       "      <td>4.8</td>\n",
       "      <td>82.0</td>\n",
       "      <td>3.19</td>\n",
       "      <td>58.0</td>\n",
       "      <td>1.3</td>\n",
       "      <td>15442.0</td>\n",
       "      <td>13277.0</td>\n",
       "      <td>...</td>\n",
       "      <td>15.04</td>\n",
       "      <td>62.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>17.6</td>\n",
       "      <td>Gross domestic product per capita, current prices</td>\n",
       "      <td>U.S. dollars</td>\n",
       "      <td>Units</td>\n",
       "      <td>See notes for:  Gross domestic product, curren...</td>\n",
       "      <td>12239.894</td>\n",
       "      <td>2015.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Poland</th>\n",
       "      <td>33.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>10.8</td>\n",
       "      <td>3.2</td>\n",
       "      <td>90.0</td>\n",
       "      <td>7.41</td>\n",
       "      <td>60.0</td>\n",
       "      <td>0.9</td>\n",
       "      <td>17852.0</td>\n",
       "      <td>10919.0</td>\n",
       "      <td>...</td>\n",
       "      <td>14.20</td>\n",
       "      <td>55.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>18.4</td>\n",
       "      <td>Gross domestic product per capita, current prices</td>\n",
       "      <td>U.S. dollars</td>\n",
       "      <td>Units</td>\n",
       "      <td>See notes for:  Gross domestic product, curren...</td>\n",
       "      <td>12495.334</td>\n",
       "      <td>2014.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chile</th>\n",
       "      <td>46.0</td>\n",
       "      <td>6.9</td>\n",
       "      <td>2.0</td>\n",
       "      <td>9.4</td>\n",
       "      <td>57.0</td>\n",
       "      <td>15.42</td>\n",
       "      <td>62.0</td>\n",
       "      <td>4.4</td>\n",
       "      <td>14533.0</td>\n",
       "      <td>17733.0</td>\n",
       "      <td>...</td>\n",
       "      <td>14.41</td>\n",
       "      <td>49.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>16.5</td>\n",
       "      <td>Gross domestic product per capita, current prices</td>\n",
       "      <td>U.S. dollars</td>\n",
       "      <td>Units</td>\n",
       "      <td>See notes for:  Gross domestic product, curren...</td>\n",
       "      <td>13340.905</td>\n",
       "      <td>2014.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Slovak Republic</th>\n",
       "      <td>13.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.6</td>\n",
       "      <td>0.6</td>\n",
       "      <td>92.0</td>\n",
       "      <td>7.02</td>\n",
       "      <td>60.0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>17503.0</td>\n",
       "      <td>8663.0</td>\n",
       "      <td>...</td>\n",
       "      <td>14.99</td>\n",
       "      <td>59.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>16.3</td>\n",
       "      <td>Gross domestic product per capita, current prices</td>\n",
       "      <td>U.S. dollars</td>\n",
       "      <td>Units</td>\n",
       "      <td>See notes for:  Gross domestic product, curren...</td>\n",
       "      <td>15991.736</td>\n",
       "      <td>2015.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Czech Republic</th>\n",
       "      <td>16.0</td>\n",
       "      <td>2.8</td>\n",
       "      <td>6.8</td>\n",
       "      <td>0.9</td>\n",
       "      <td>92.0</td>\n",
       "      <td>6.98</td>\n",
       "      <td>68.0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>18404.0</td>\n",
       "      <td>17299.0</td>\n",
       "      <td>...</td>\n",
       "      <td>14.98</td>\n",
       "      <td>59.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>18.1</td>\n",
       "      <td>Gross domestic product per capita, current prices</td>\n",
       "      <td>U.S. dollars</td>\n",
       "      <td>Units</td>\n",
       "      <td>See notes for:  Gross domestic product, curren...</td>\n",
       "      <td>17256.918</td>\n",
       "      <td>2015.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Estonia</th>\n",
       "      <td>9.0</td>\n",
       "      <td>5.5</td>\n",
       "      <td>3.3</td>\n",
       "      <td>8.1</td>\n",
       "      <td>90.0</td>\n",
       "      <td>3.30</td>\n",
       "      <td>68.0</td>\n",
       "      <td>4.8</td>\n",
       "      <td>15167.0</td>\n",
       "      <td>7680.0</td>\n",
       "      <td>...</td>\n",
       "      <td>14.90</td>\n",
       "      <td>64.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>17.5</td>\n",
       "      <td>Gross domestic product per capita, current prices</td>\n",
       "      <td>U.S. dollars</td>\n",
       "      <td>Units</td>\n",
       "      <td>See notes for:  Gross domestic product, curren...</td>\n",
       "      <td>17288.083</td>\n",
       "      <td>2014.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Greece</th>\n",
       "      <td>27.0</td>\n",
       "      <td>3.7</td>\n",
       "      <td>6.5</td>\n",
       "      <td>0.7</td>\n",
       "      <td>68.0</td>\n",
       "      <td>6.16</td>\n",
       "      <td>49.0</td>\n",
       "      <td>1.6</td>\n",
       "      <td>18575.0</td>\n",
       "      <td>14579.0</td>\n",
       "      <td>...</td>\n",
       "      <td>14.91</td>\n",
       "      <td>64.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>18.6</td>\n",
       "      <td>Gross domestic product per capita, current prices</td>\n",
       "      <td>U.S. dollars</td>\n",
       "      <td>Units</td>\n",
       "      <td>See notes for:  Gross domestic product, curren...</td>\n",
       "      <td>18064.288</td>\n",
       "      <td>2014.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Portugal</th>\n",
       "      <td>18.0</td>\n",
       "      <td>5.7</td>\n",
       "      <td>6.5</td>\n",
       "      <td>0.9</td>\n",
       "      <td>38.0</td>\n",
       "      <td>9.62</td>\n",
       "      <td>61.0</td>\n",
       "      <td>1.1</td>\n",
       "      <td>20086.0</td>\n",
       "      <td>31245.0</td>\n",
       "      <td>...</td>\n",
       "      <td>14.95</td>\n",
       "      <td>58.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>17.6</td>\n",
       "      <td>Gross domestic product per capita, current prices</td>\n",
       "      <td>U.S. dollars</td>\n",
       "      <td>Units</td>\n",
       "      <td>See notes for:  Gross domestic product, curren...</td>\n",
       "      <td>19121.592</td>\n",
       "      <td>2014.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Slovenia</th>\n",
       "      <td>26.0</td>\n",
       "      <td>3.9</td>\n",
       "      <td>10.3</td>\n",
       "      <td>0.5</td>\n",
       "      <td>85.0</td>\n",
       "      <td>5.63</td>\n",
       "      <td>63.0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>19326.0</td>\n",
       "      <td>18465.0</td>\n",
       "      <td>...</td>\n",
       "      <td>14.62</td>\n",
       "      <td>52.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>18.4</td>\n",
       "      <td>Gross domestic product per capita, current prices</td>\n",
       "      <td>U.S. dollars</td>\n",
       "      <td>Units</td>\n",
       "      <td>See notes for:  Gross domestic product, curren...</td>\n",
       "      <td>20732.482</td>\n",
       "      <td>2015.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Spain</th>\n",
       "      <td>24.0</td>\n",
       "      <td>4.2</td>\n",
       "      <td>7.3</td>\n",
       "      <td>0.1</td>\n",
       "      <td>55.0</td>\n",
       "      <td>5.89</td>\n",
       "      <td>56.0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>22477.0</td>\n",
       "      <td>24774.0</td>\n",
       "      <td>...</td>\n",
       "      <td>16.06</td>\n",
       "      <td>69.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>17.6</td>\n",
       "      <td>Gross domestic product per capita, current prices</td>\n",
       "      <td>U.S. dollars</td>\n",
       "      <td>Units</td>\n",
       "      <td>See notes for:  Gross domestic product, curren...</td>\n",
       "      <td>25864.721</td>\n",
       "      <td>2014.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Korea</th>\n",
       "      <td>30.0</td>\n",
       "      <td>2.1</td>\n",
       "      <td>10.4</td>\n",
       "      <td>4.2</td>\n",
       "      <td>82.0</td>\n",
       "      <td>18.72</td>\n",
       "      <td>64.0</td>\n",
       "      <td>1.1</td>\n",
       "      <td>19510.0</td>\n",
       "      <td>29091.0</td>\n",
       "      <td>...</td>\n",
       "      <td>14.63</td>\n",
       "      <td>76.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>17.5</td>\n",
       "      <td>Gross domestic product per capita, current prices</td>\n",
       "      <td>U.S. dollars</td>\n",
       "      <td>Units</td>\n",
       "      <td>See notes for:  Gross domestic product, curren...</td>\n",
       "      <td>27195.197</td>\n",
       "      <td>2014.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Italy</th>\n",
       "      <td>21.0</td>\n",
       "      <td>4.7</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.1</td>\n",
       "      <td>57.0</td>\n",
       "      <td>3.66</td>\n",
       "      <td>56.0</td>\n",
       "      <td>0.7</td>\n",
       "      <td>25166.0</td>\n",
       "      <td>54987.0</td>\n",
       "      <td>...</td>\n",
       "      <td>14.98</td>\n",
       "      <td>75.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>16.8</td>\n",
       "      <td>Gross domestic product per capita, current prices</td>\n",
       "      <td>U.S. dollars</td>\n",
       "      <td>Units</td>\n",
       "      <td>See notes for:  Gross domestic product, curren...</td>\n",
       "      <td>29866.581</td>\n",
       "      <td>2015.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Japan</th>\n",
       "      <td>24.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>7.3</td>\n",
       "      <td>6.4</td>\n",
       "      <td>94.0</td>\n",
       "      <td>22.26</td>\n",
       "      <td>72.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>26111.0</td>\n",
       "      <td>86764.0</td>\n",
       "      <td>...</td>\n",
       "      <td>14.93</td>\n",
       "      <td>53.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>16.3</td>\n",
       "      <td>Gross domestic product per capita, current prices</td>\n",
       "      <td>U.S. dollars</td>\n",
       "      <td>Units</td>\n",
       "      <td>See notes for:  Gross domestic product, curren...</td>\n",
       "      <td>32485.545</td>\n",
       "      <td>2015.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Israel</th>\n",
       "      <td>21.0</td>\n",
       "      <td>6.4</td>\n",
       "      <td>2.5</td>\n",
       "      <td>3.7</td>\n",
       "      <td>85.0</td>\n",
       "      <td>16.03</td>\n",
       "      <td>67.0</td>\n",
       "      <td>2.3</td>\n",
       "      <td>22104.0</td>\n",
       "      <td>52933.0</td>\n",
       "      <td>...</td>\n",
       "      <td>14.48</td>\n",
       "      <td>68.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>15.8</td>\n",
       "      <td>Gross domestic product per capita, current prices</td>\n",
       "      <td>U.S. dollars</td>\n",
       "      <td>Units</td>\n",
       "      <td>See notes for:  Gross domestic product, curren...</td>\n",
       "      <td>35343.336</td>\n",
       "      <td>2015.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New Zealand</th>\n",
       "      <td>11.0</td>\n",
       "      <td>2.2</td>\n",
       "      <td>10.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>74.0</td>\n",
       "      <td>13.87</td>\n",
       "      <td>73.0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>23815.0</td>\n",
       "      <td>28290.0</td>\n",
       "      <td>...</td>\n",
       "      <td>14.87</td>\n",
       "      <td>77.0</td>\n",
       "      <td>89.0</td>\n",
       "      <td>18.1</td>\n",
       "      <td>Gross domestic product per capita, current prices</td>\n",
       "      <td>U.S. dollars</td>\n",
       "      <td>Units</td>\n",
       "      <td>See notes for:  Gross domestic product, curren...</td>\n",
       "      <td>37044.891</td>\n",
       "      <td>2015.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>France</th>\n",
       "      <td>12.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>73.0</td>\n",
       "      <td>8.15</td>\n",
       "      <td>64.0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>28799.0</td>\n",
       "      <td>48741.0</td>\n",
       "      <td>...</td>\n",
       "      <td>15.33</td>\n",
       "      <td>80.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>16.4</td>\n",
       "      <td>Gross domestic product per capita, current prices</td>\n",
       "      <td>U.S. dollars</td>\n",
       "      <td>Units</td>\n",
       "      <td>See notes for:  Gross domestic product, curren...</td>\n",
       "      <td>37675.006</td>\n",
       "      <td>2015.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Belgium</th>\n",
       "      <td>21.0</td>\n",
       "      <td>6.6</td>\n",
       "      <td>4.5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>4.57</td>\n",
       "      <td>62.0</td>\n",
       "      <td>1.1</td>\n",
       "      <td>28307.0</td>\n",
       "      <td>83876.0</td>\n",
       "      <td>...</td>\n",
       "      <td>15.71</td>\n",
       "      <td>89.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>18.9</td>\n",
       "      <td>Gross domestic product per capita, current prices</td>\n",
       "      <td>U.S. dollars</td>\n",
       "      <td>Units</td>\n",
       "      <td>See notes for:  Gross domestic product, curren...</td>\n",
       "      <td>40106.632</td>\n",
       "      <td>2014.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Germany</th>\n",
       "      <td>16.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>4.5</td>\n",
       "      <td>0.1</td>\n",
       "      <td>86.0</td>\n",
       "      <td>5.25</td>\n",
       "      <td>73.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>31252.0</td>\n",
       "      <td>50394.0</td>\n",
       "      <td>...</td>\n",
       "      <td>15.31</td>\n",
       "      <td>72.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>18.2</td>\n",
       "      <td>Gross domestic product per capita, current prices</td>\n",
       "      <td>U.S. dollars</td>\n",
       "      <td>Units</td>\n",
       "      <td>See notes for:  Gross domestic product, curren...</td>\n",
       "      <td>40996.511</td>\n",
       "      <td>2014.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Finland</th>\n",
       "      <td>15.0</td>\n",
       "      <td>2.4</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>85.0</td>\n",
       "      <td>3.58</td>\n",
       "      <td>69.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>27927.0</td>\n",
       "      <td>18761.0</td>\n",
       "      <td>...</td>\n",
       "      <td>14.89</td>\n",
       "      <td>69.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>19.7</td>\n",
       "      <td>Gross domestic product per capita, current prices</td>\n",
       "      <td>U.S. dollars</td>\n",
       "      <td>Units</td>\n",
       "      <td>See notes for:  Gross domestic product, curren...</td>\n",
       "      <td>41973.988</td>\n",
       "      <td>2014.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Canada</th>\n",
       "      <td>15.0</td>\n",
       "      <td>1.3</td>\n",
       "      <td>10.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>89.0</td>\n",
       "      <td>3.94</td>\n",
       "      <td>72.0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>29365.0</td>\n",
       "      <td>67913.0</td>\n",
       "      <td>...</td>\n",
       "      <td>14.25</td>\n",
       "      <td>61.0</td>\n",
       "      <td>91.0</td>\n",
       "      <td>17.2</td>\n",
       "      <td>Gross domestic product per capita, current prices</td>\n",
       "      <td>U.S. dollars</td>\n",
       "      <td>Units</td>\n",
       "      <td>See notes for:  Gross domestic product, curren...</td>\n",
       "      <td>43331.961</td>\n",
       "      <td>2015.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Netherlands</th>\n",
       "      <td>30.0</td>\n",
       "      <td>4.9</td>\n",
       "      <td>6.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>0.45</td>\n",
       "      <td>74.0</td>\n",
       "      <td>0.9</td>\n",
       "      <td>27888.0</td>\n",
       "      <td>77961.0</td>\n",
       "      <td>...</td>\n",
       "      <td>15.44</td>\n",
       "      <td>75.0</td>\n",
       "      <td>92.0</td>\n",
       "      <td>18.7</td>\n",
       "      <td>Gross domestic product per capita, current prices</td>\n",
       "      <td>U.S. dollars</td>\n",
       "      <td>Units</td>\n",
       "      <td>See notes for:  Gross domestic product, curren...</td>\n",
       "      <td>43603.115</td>\n",
       "      <td>2014.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Austria</th>\n",
       "      <td>27.0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>7.1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>83.0</td>\n",
       "      <td>7.61</td>\n",
       "      <td>72.0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>31173.0</td>\n",
       "      <td>49887.0</td>\n",
       "      <td>...</td>\n",
       "      <td>14.46</td>\n",
       "      <td>75.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>Gross domestic product per capita, current prices</td>\n",
       "      <td>U.S. dollars</td>\n",
       "      <td>Units</td>\n",
       "      <td>See notes for:  Gross domestic product, curren...</td>\n",
       "      <td>43724.031</td>\n",
       "      <td>2015.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United Kingdom</th>\n",
       "      <td>13.0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>11.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>78.0</td>\n",
       "      <td>12.70</td>\n",
       "      <td>71.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>27029.0</td>\n",
       "      <td>60778.0</td>\n",
       "      <td>...</td>\n",
       "      <td>14.83</td>\n",
       "      <td>66.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>16.4</td>\n",
       "      <td>Gross domestic product per capita, current prices</td>\n",
       "      <td>U.S. dollars</td>\n",
       "      <td>Units</td>\n",
       "      <td>See notes for:  Gross domestic product, curren...</td>\n",
       "      <td>43770.688</td>\n",
       "      <td>2015.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sweden</th>\n",
       "      <td>10.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>10.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>1.13</td>\n",
       "      <td>74.0</td>\n",
       "      <td>0.7</td>\n",
       "      <td>29185.0</td>\n",
       "      <td>60328.0</td>\n",
       "      <td>...</td>\n",
       "      <td>15.11</td>\n",
       "      <td>86.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>19.3</td>\n",
       "      <td>Gross domestic product per capita, current prices</td>\n",
       "      <td>U.S. dollars</td>\n",
       "      <td>Units</td>\n",
       "      <td>See notes for:  Gross domestic product, curren...</td>\n",
       "      <td>49866.266</td>\n",
       "      <td>2014.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Iceland</th>\n",
       "      <td>18.0</td>\n",
       "      <td>2.7</td>\n",
       "      <td>5.1</td>\n",
       "      <td>0.4</td>\n",
       "      <td>71.0</td>\n",
       "      <td>12.25</td>\n",
       "      <td>82.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>23965.0</td>\n",
       "      <td>43045.0</td>\n",
       "      <td>...</td>\n",
       "      <td>14.61</td>\n",
       "      <td>81.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>19.8</td>\n",
       "      <td>Gross domestic product per capita, current prices</td>\n",
       "      <td>U.S. dollars</td>\n",
       "      <td>Units</td>\n",
       "      <td>See notes for:  Gross domestic product, curren...</td>\n",
       "      <td>50854.583</td>\n",
       "      <td>2014.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Australia</th>\n",
       "      <td>13.0</td>\n",
       "      <td>2.1</td>\n",
       "      <td>10.5</td>\n",
       "      <td>1.1</td>\n",
       "      <td>76.0</td>\n",
       "      <td>14.02</td>\n",
       "      <td>72.0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>31588.0</td>\n",
       "      <td>47657.0</td>\n",
       "      <td>...</td>\n",
       "      <td>14.41</td>\n",
       "      <td>93.0</td>\n",
       "      <td>91.0</td>\n",
       "      <td>19.4</td>\n",
       "      <td>Gross domestic product per capita, current prices</td>\n",
       "      <td>U.S. dollars</td>\n",
       "      <td>Units</td>\n",
       "      <td>See notes for:  Gross domestic product, curren...</td>\n",
       "      <td>50961.865</td>\n",
       "      <td>2014.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ireland</th>\n",
       "      <td>13.0</td>\n",
       "      <td>2.6</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>75.0</td>\n",
       "      <td>4.20</td>\n",
       "      <td>60.0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>23917.0</td>\n",
       "      <td>31580.0</td>\n",
       "      <td>...</td>\n",
       "      <td>15.19</td>\n",
       "      <td>70.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>17.6</td>\n",
       "      <td>Gross domestic product per capita, current prices</td>\n",
       "      <td>U.S. dollars</td>\n",
       "      <td>Units</td>\n",
       "      <td>See notes for:  Gross domestic product, curren...</td>\n",
       "      <td>51350.744</td>\n",
       "      <td>2014.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Denmark</th>\n",
       "      <td>15.0</td>\n",
       "      <td>3.9</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.9</td>\n",
       "      <td>78.0</td>\n",
       "      <td>2.03</td>\n",
       "      <td>73.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>26491.0</td>\n",
       "      <td>44488.0</td>\n",
       "      <td>...</td>\n",
       "      <td>16.06</td>\n",
       "      <td>88.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>19.4</td>\n",
       "      <td>Gross domestic product per capita, current prices</td>\n",
       "      <td>U.S. dollars</td>\n",
       "      <td>Units</td>\n",
       "      <td>See notes for:  Gross domestic product, curren...</td>\n",
       "      <td>52114.165</td>\n",
       "      <td>2015.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United States</th>\n",
       "      <td>18.0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>8.3</td>\n",
       "      <td>0.1</td>\n",
       "      <td>89.0</td>\n",
       "      <td>11.30</td>\n",
       "      <td>67.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>41355.0</td>\n",
       "      <td>145769.0</td>\n",
       "      <td>...</td>\n",
       "      <td>14.27</td>\n",
       "      <td>68.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>17.2</td>\n",
       "      <td>Gross domestic product per capita, current prices</td>\n",
       "      <td>U.S. dollars</td>\n",
       "      <td>Units</td>\n",
       "      <td>See notes for:  Gross domestic product, curren...</td>\n",
       "      <td>55805.204</td>\n",
       "      <td>2015.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Norway</th>\n",
       "      <td>16.0</td>\n",
       "      <td>3.3</td>\n",
       "      <td>8.1</td>\n",
       "      <td>0.3</td>\n",
       "      <td>82.0</td>\n",
       "      <td>2.82</td>\n",
       "      <td>75.0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>33492.0</td>\n",
       "      <td>8797.0</td>\n",
       "      <td>...</td>\n",
       "      <td>15.56</td>\n",
       "      <td>78.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>17.9</td>\n",
       "      <td>Gross domestic product per capita, current prices</td>\n",
       "      <td>U.S. dollars</td>\n",
       "      <td>Units</td>\n",
       "      <td>See notes for:  Gross domestic product, curren...</td>\n",
       "      <td>74822.106</td>\n",
       "      <td>2015.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Switzerland</th>\n",
       "      <td>20.0</td>\n",
       "      <td>4.2</td>\n",
       "      <td>8.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>6.72</td>\n",
       "      <td>80.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>33491.0</td>\n",
       "      <td>108823.0</td>\n",
       "      <td>...</td>\n",
       "      <td>14.98</td>\n",
       "      <td>49.0</td>\n",
       "      <td>96.0</td>\n",
       "      <td>17.3</td>\n",
       "      <td>Gross domestic product per capita, current prices</td>\n",
       "      <td>U.S. dollars</td>\n",
       "      <td>Units</td>\n",
       "      <td>See notes for:  Gross domestic product, curren...</td>\n",
       "      <td>80675.308</td>\n",
       "      <td>2015.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Luxembourg</th>\n",
       "      <td>12.0</td>\n",
       "      <td>4.3</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>78.0</td>\n",
       "      <td>3.47</td>\n",
       "      <td>66.0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>38951.0</td>\n",
       "      <td>61765.0</td>\n",
       "      <td>...</td>\n",
       "      <td>15.12</td>\n",
       "      <td>91.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>15.1</td>\n",
       "      <td>Gross domestic product per capita, current prices</td>\n",
       "      <td>U.S. dollars</td>\n",
       "      <td>Units</td>\n",
       "      <td>See notes for:  Gross domestic product, curren...</td>\n",
       "      <td>101994.093</td>\n",
       "      <td>2014.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>36 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Air pollution  Assault rate  Consultation on rule-making  \\\n",
       "Country                                                                     \n",
       "Brazil                    18.0           7.9                          4.0   \n",
       "Mexico                    30.0          12.8                          9.0   \n",
       "Russia                    15.0           3.8                          2.5   \n",
       "Turkey                    35.0           5.0                          5.5   \n",
       "Hungary                   15.0           3.6                          7.9   \n",
       "Poland                    33.0           1.4                         10.8   \n",
       "Chile                     46.0           6.9                          2.0   \n",
       "Slovak Republic           13.0           3.0                          6.6   \n",
       "Czech Republic            16.0           2.8                          6.8   \n",
       "Estonia                    9.0           5.5                          3.3   \n",
       "Greece                    27.0           3.7                          6.5   \n",
       "Portugal                  18.0           5.7                          6.5   \n",
       "Slovenia                  26.0           3.9                         10.3   \n",
       "Spain                     24.0           4.2                          7.3   \n",
       "Korea                     30.0           2.1                         10.4   \n",
       "Italy                     21.0           4.7                          5.0   \n",
       "Japan                     24.0           1.4                          7.3   \n",
       "Israel                    21.0           6.4                          2.5   \n",
       "New Zealand               11.0           2.2                         10.3   \n",
       "France                    12.0           5.0                          3.5   \n",
       "Belgium                   21.0           6.6                          4.5   \n",
       "Germany                   16.0           3.6                          4.5   \n",
       "Finland                   15.0           2.4                          9.0   \n",
       "Canada                    15.0           1.3                         10.5   \n",
       "Netherlands               30.0           4.9                          6.1   \n",
       "Austria                   27.0           3.4                          7.1   \n",
       "United Kingdom            13.0           1.9                         11.5   \n",
       "Sweden                    10.0           5.1                         10.9   \n",
       "Iceland                   18.0           2.7                          5.1   \n",
       "Australia                 13.0           2.1                         10.5   \n",
       "Ireland                   13.0           2.6                          9.0   \n",
       "Denmark                   15.0           3.9                          7.0   \n",
       "United States             18.0           1.5                          8.3   \n",
       "Norway                    16.0           3.3                          8.1   \n",
       "Switzerland               20.0           4.2                          8.4   \n",
       "Luxembourg                12.0           4.3                          6.0   \n",
       "\n",
       "                 Dwellings without basic facilities  Educational attainment  \\\n",
       "Country                                                                       \n",
       "Brazil                                          6.7                    45.0   \n",
       "Mexico                                          4.2                    37.0   \n",
       "Russia                                         15.1                    94.0   \n",
       "Turkey                                         12.7                    34.0   \n",
       "Hungary                                         4.8                    82.0   \n",
       "Poland                                          3.2                    90.0   \n",
       "Chile                                           9.4                    57.0   \n",
       "Slovak Republic                                 0.6                    92.0   \n",
       "Czech Republic                                  0.9                    92.0   \n",
       "Estonia                                         8.1                    90.0   \n",
       "Greece                                          0.7                    68.0   \n",
       "Portugal                                        0.9                    38.0   \n",
       "Slovenia                                        0.5                    85.0   \n",
       "Spain                                           0.1                    55.0   \n",
       "Korea                                           4.2                    82.0   \n",
       "Italy                                           1.1                    57.0   \n",
       "Japan                                           6.4                    94.0   \n",
       "Israel                                          3.7                    85.0   \n",
       "New Zealand                                     0.2                    74.0   \n",
       "France                                          0.5                    73.0   \n",
       "Belgium                                         2.0                    72.0   \n",
       "Germany                                         0.1                    86.0   \n",
       "Finland                                         0.6                    85.0   \n",
       "Canada                                          0.2                    89.0   \n",
       "Netherlands                                     0.0                    73.0   \n",
       "Austria                                         1.0                    83.0   \n",
       "United Kingdom                                  0.2                    78.0   \n",
       "Sweden                                          0.0                    88.0   \n",
       "Iceland                                         0.4                    71.0   \n",
       "Australia                                       1.1                    76.0   \n",
       "Ireland                                         0.2                    75.0   \n",
       "Denmark                                         0.9                    78.0   \n",
       "United States                                   0.1                    89.0   \n",
       "Norway                                          0.3                    82.0   \n",
       "Switzerland                                     0.0                    86.0   \n",
       "Luxembourg                                      0.1                    78.0   \n",
       "\n",
       "                 Employees working very long hours  Employment rate  \\\n",
       "Country                                                               \n",
       "Brazil                                       10.41             67.0   \n",
       "Mexico                                       28.83             61.0   \n",
       "Russia                                        0.16             69.0   \n",
       "Turkey                                       40.86             50.0   \n",
       "Hungary                                       3.19             58.0   \n",
       "Poland                                        7.41             60.0   \n",
       "Chile                                        15.42             62.0   \n",
       "Slovak Republic                               7.02             60.0   \n",
       "Czech Republic                                6.98             68.0   \n",
       "Estonia                                       3.30             68.0   \n",
       "Greece                                        6.16             49.0   \n",
       "Portugal                                      9.62             61.0   \n",
       "Slovenia                                      5.63             63.0   \n",
       "Spain                                         5.89             56.0   \n",
       "Korea                                        18.72             64.0   \n",
       "Italy                                         3.66             56.0   \n",
       "Japan                                        22.26             72.0   \n",
       "Israel                                       16.03             67.0   \n",
       "New Zealand                                  13.87             73.0   \n",
       "France                                        8.15             64.0   \n",
       "Belgium                                       4.57             62.0   \n",
       "Germany                                       5.25             73.0   \n",
       "Finland                                       3.58             69.0   \n",
       "Canada                                        3.94             72.0   \n",
       "Netherlands                                   0.45             74.0   \n",
       "Austria                                       7.61             72.0   \n",
       "United Kingdom                               12.70             71.0   \n",
       "Sweden                                        1.13             74.0   \n",
       "Iceland                                      12.25             82.0   \n",
       "Australia                                    14.02             72.0   \n",
       "Ireland                                       4.20             60.0   \n",
       "Denmark                                       2.03             73.0   \n",
       "United States                                11.30             67.0   \n",
       "Norway                                        2.82             75.0   \n",
       "Switzerland                                   6.72             80.0   \n",
       "Luxembourg                                    3.47             66.0   \n",
       "\n",
       "                 Homicide rate  Household net adjusted disposable income  \\\n",
       "Country                                                                    \n",
       "Brazil                    25.5                                   11664.0   \n",
       "Mexico                    23.4                                   13085.0   \n",
       "Russia                    12.8                                   19292.0   \n",
       "Turkey                     1.2                                   14095.0   \n",
       "Hungary                    1.3                                   15442.0   \n",
       "Poland                     0.9                                   17852.0   \n",
       "Chile                      4.4                                   14533.0   \n",
       "Slovak Republic            1.2                                   17503.0   \n",
       "Czech Republic             0.8                                   18404.0   \n",
       "Estonia                    4.8                                   15167.0   \n",
       "Greece                     1.6                                   18575.0   \n",
       "Portugal                   1.1                                   20086.0   \n",
       "Slovenia                   0.4                                   19326.0   \n",
       "Spain                      0.6                                   22477.0   \n",
       "Korea                      1.1                                   19510.0   \n",
       "Italy                      0.7                                   25166.0   \n",
       "Japan                      0.3                                   26111.0   \n",
       "Israel                     2.3                                   22104.0   \n",
       "New Zealand                1.2                                   23815.0   \n",
       "France                     0.6                                   28799.0   \n",
       "Belgium                    1.1                                   28307.0   \n",
       "Germany                    0.5                                   31252.0   \n",
       "Finland                    1.4                                   27927.0   \n",
       "Canada                     1.5                                   29365.0   \n",
       "Netherlands                0.9                                   27888.0   \n",
       "Austria                    0.4                                   31173.0   \n",
       "United Kingdom             0.3                                   27029.0   \n",
       "Sweden                     0.7                                   29185.0   \n",
       "Iceland                    0.3                                   23965.0   \n",
       "Australia                  0.8                                   31588.0   \n",
       "Ireland                    0.8                                   23917.0   \n",
       "Denmark                    0.3                                   26491.0   \n",
       "United States              5.2                                   41355.0   \n",
       "Norway                     0.6                                   33492.0   \n",
       "Switzerland                0.5                                   33491.0   \n",
       "Luxembourg                 0.4                                   38951.0   \n",
       "\n",
       "                 Household net financial wealth  ...  \\\n",
       "Country                                          ...   \n",
       "Brazil                                   6844.0  ...   \n",
       "Mexico                                   9056.0  ...   \n",
       "Russia                                   3412.0  ...   \n",
       "Turkey                                   3251.0  ...   \n",
       "Hungary                                 13277.0  ...   \n",
       "Poland                                  10919.0  ...   \n",
       "Chile                                   17733.0  ...   \n",
       "Slovak Republic                          8663.0  ...   \n",
       "Czech Republic                          17299.0  ...   \n",
       "Estonia                                  7680.0  ...   \n",
       "Greece                                  14579.0  ...   \n",
       "Portugal                                31245.0  ...   \n",
       "Slovenia                                18465.0  ...   \n",
       "Spain                                   24774.0  ...   \n",
       "Korea                                   29091.0  ...   \n",
       "Italy                                   54987.0  ...   \n",
       "Japan                                   86764.0  ...   \n",
       "Israel                                  52933.0  ...   \n",
       "New Zealand                             28290.0  ...   \n",
       "France                                  48741.0  ...   \n",
       "Belgium                                 83876.0  ...   \n",
       "Germany                                 50394.0  ...   \n",
       "Finland                                 18761.0  ...   \n",
       "Canada                                  67913.0  ...   \n",
       "Netherlands                             77961.0  ...   \n",
       "Austria                                 49887.0  ...   \n",
       "United Kingdom                          60778.0  ...   \n",
       "Sweden                                  60328.0  ...   \n",
       "Iceland                                 43045.0  ...   \n",
       "Australia                               47657.0  ...   \n",
       "Ireland                                 31580.0  ...   \n",
       "Denmark                                 44488.0  ...   \n",
       "United States                          145769.0  ...   \n",
       "Norway                                   8797.0  ...   \n",
       "Switzerland                            108823.0  ...   \n",
       "Luxembourg                              61765.0  ...   \n",
       "\n",
       "                 Time devoted to leisure and personal care  Voter turnout  \\\n",
       "Country                                                                     \n",
       "Brazil                                               14.97           79.0   \n",
       "Mexico                                               13.89           63.0   \n",
       "Russia                                               14.97           65.0   \n",
       "Turkey                                               13.42           88.0   \n",
       "Hungary                                              15.04           62.0   \n",
       "Poland                                               14.20           55.0   \n",
       "Chile                                                14.41           49.0   \n",
       "Slovak Republic                                      14.99           59.0   \n",
       "Czech Republic                                       14.98           59.0   \n",
       "Estonia                                              14.90           64.0   \n",
       "Greece                                               14.91           64.0   \n",
       "Portugal                                             14.95           58.0   \n",
       "Slovenia                                             14.62           52.0   \n",
       "Spain                                                16.06           69.0   \n",
       "Korea                                                14.63           76.0   \n",
       "Italy                                                14.98           75.0   \n",
       "Japan                                                14.93           53.0   \n",
       "Israel                                               14.48           68.0   \n",
       "New Zealand                                          14.87           77.0   \n",
       "France                                               15.33           80.0   \n",
       "Belgium                                              15.71           89.0   \n",
       "Germany                                              15.31           72.0   \n",
       "Finland                                              14.89           69.0   \n",
       "Canada                                               14.25           61.0   \n",
       "Netherlands                                          15.44           75.0   \n",
       "Austria                                              14.46           75.0   \n",
       "United Kingdom                                       14.83           66.0   \n",
       "Sweden                                               15.11           86.0   \n",
       "Iceland                                              14.61           81.0   \n",
       "Australia                                            14.41           93.0   \n",
       "Ireland                                              15.19           70.0   \n",
       "Denmark                                              16.06           88.0   \n",
       "United States                                        14.27           68.0   \n",
       "Norway                                               15.56           78.0   \n",
       "Switzerland                                          14.98           49.0   \n",
       "Luxembourg                                           15.12           91.0   \n",
       "\n",
       "                 Water quality  Years in education  \\\n",
       "Country                                              \n",
       "Brazil                    72.0                16.3   \n",
       "Mexico                    67.0                14.4   \n",
       "Russia                    56.0                16.0   \n",
       "Turkey                    62.0                16.4   \n",
       "Hungary                   77.0                17.6   \n",
       "Poland                    79.0                18.4   \n",
       "Chile                     73.0                16.5   \n",
       "Slovak Republic           81.0                16.3   \n",
       "Czech Republic            85.0                18.1   \n",
       "Estonia                   79.0                17.5   \n",
       "Greece                    69.0                18.6   \n",
       "Portugal                  86.0                17.6   \n",
       "Slovenia                  88.0                18.4   \n",
       "Spain                     71.0                17.6   \n",
       "Korea                     78.0                17.5   \n",
       "Italy                     71.0                16.8   \n",
       "Japan                     85.0                16.3   \n",
       "Israel                    68.0                15.8   \n",
       "New Zealand               89.0                18.1   \n",
       "France                    82.0                16.4   \n",
       "Belgium                   87.0                18.9   \n",
       "Germany                   95.0                18.2   \n",
       "Finland                   94.0                19.7   \n",
       "Canada                    91.0                17.2   \n",
       "Netherlands               92.0                18.7   \n",
       "Austria                   94.0                17.0   \n",
       "United Kingdom            88.0                16.4   \n",
       "Sweden                    95.0                19.3   \n",
       "Iceland                   97.0                19.8   \n",
       "Australia                 91.0                19.4   \n",
       "Ireland                   80.0                17.6   \n",
       "Denmark                   94.0                19.4   \n",
       "United States             85.0                17.2   \n",
       "Norway                    94.0                17.9   \n",
       "Switzerland               96.0                17.3   \n",
       "Luxembourg                86.0                15.1   \n",
       "\n",
       "                                                Subject Descriptor  \\\n",
       "Country                                                              \n",
       "Brazil           Gross domestic product per capita, current prices   \n",
       "Mexico           Gross domestic product per capita, current prices   \n",
       "Russia           Gross domestic product per capita, current prices   \n",
       "Turkey           Gross domestic product per capita, current prices   \n",
       "Hungary          Gross domestic product per capita, current prices   \n",
       "Poland           Gross domestic product per capita, current prices   \n",
       "Chile            Gross domestic product per capita, current prices   \n",
       "Slovak Republic  Gross domestic product per capita, current prices   \n",
       "Czech Republic   Gross domestic product per capita, current prices   \n",
       "Estonia          Gross domestic product per capita, current prices   \n",
       "Greece           Gross domestic product per capita, current prices   \n",
       "Portugal         Gross domestic product per capita, current prices   \n",
       "Slovenia         Gross domestic product per capita, current prices   \n",
       "Spain            Gross domestic product per capita, current prices   \n",
       "Korea            Gross domestic product per capita, current prices   \n",
       "Italy            Gross domestic product per capita, current prices   \n",
       "Japan            Gross domestic product per capita, current prices   \n",
       "Israel           Gross domestic product per capita, current prices   \n",
       "New Zealand      Gross domestic product per capita, current prices   \n",
       "France           Gross domestic product per capita, current prices   \n",
       "Belgium          Gross domestic product per capita, current prices   \n",
       "Germany          Gross domestic product per capita, current prices   \n",
       "Finland          Gross domestic product per capita, current prices   \n",
       "Canada           Gross domestic product per capita, current prices   \n",
       "Netherlands      Gross domestic product per capita, current prices   \n",
       "Austria          Gross domestic product per capita, current prices   \n",
       "United Kingdom   Gross domestic product per capita, current prices   \n",
       "Sweden           Gross domestic product per capita, current prices   \n",
       "Iceland          Gross domestic product per capita, current prices   \n",
       "Australia        Gross domestic product per capita, current prices   \n",
       "Ireland          Gross domestic product per capita, current prices   \n",
       "Denmark          Gross domestic product per capita, current prices   \n",
       "United States    Gross domestic product per capita, current prices   \n",
       "Norway           Gross domestic product per capita, current prices   \n",
       "Switzerland      Gross domestic product per capita, current prices   \n",
       "Luxembourg       Gross domestic product per capita, current prices   \n",
       "\n",
       "                        Units  Scale  \\\n",
       "Country                                \n",
       "Brazil           U.S. dollars  Units   \n",
       "Mexico           U.S. dollars  Units   \n",
       "Russia           U.S. dollars  Units   \n",
       "Turkey           U.S. dollars  Units   \n",
       "Hungary          U.S. dollars  Units   \n",
       "Poland           U.S. dollars  Units   \n",
       "Chile            U.S. dollars  Units   \n",
       "Slovak Republic  U.S. dollars  Units   \n",
       "Czech Republic   U.S. dollars  Units   \n",
       "Estonia          U.S. dollars  Units   \n",
       "Greece           U.S. dollars  Units   \n",
       "Portugal         U.S. dollars  Units   \n",
       "Slovenia         U.S. dollars  Units   \n",
       "Spain            U.S. dollars  Units   \n",
       "Korea            U.S. dollars  Units   \n",
       "Italy            U.S. dollars  Units   \n",
       "Japan            U.S. dollars  Units   \n",
       "Israel           U.S. dollars  Units   \n",
       "New Zealand      U.S. dollars  Units   \n",
       "France           U.S. dollars  Units   \n",
       "Belgium          U.S. dollars  Units   \n",
       "Germany          U.S. dollars  Units   \n",
       "Finland          U.S. dollars  Units   \n",
       "Canada           U.S. dollars  Units   \n",
       "Netherlands      U.S. dollars  Units   \n",
       "Austria          U.S. dollars  Units   \n",
       "United Kingdom   U.S. dollars  Units   \n",
       "Sweden           U.S. dollars  Units   \n",
       "Iceland          U.S. dollars  Units   \n",
       "Australia        U.S. dollars  Units   \n",
       "Ireland          U.S. dollars  Units   \n",
       "Denmark          U.S. dollars  Units   \n",
       "United States    U.S. dollars  Units   \n",
       "Norway           U.S. dollars  Units   \n",
       "Switzerland      U.S. dollars  Units   \n",
       "Luxembourg       U.S. dollars  Units   \n",
       "\n",
       "                                     Country/Series-specific Notes  \\\n",
       "Country                                                              \n",
       "Brazil           See notes for:  Gross domestic product, curren...   \n",
       "Mexico           See notes for:  Gross domestic product, curren...   \n",
       "Russia           See notes for:  Gross domestic product, curren...   \n",
       "Turkey           See notes for:  Gross domestic product, curren...   \n",
       "Hungary          See notes for:  Gross domestic product, curren...   \n",
       "Poland           See notes for:  Gross domestic product, curren...   \n",
       "Chile            See notes for:  Gross domestic product, curren...   \n",
       "Slovak Republic  See notes for:  Gross domestic product, curren...   \n",
       "Czech Republic   See notes for:  Gross domestic product, curren...   \n",
       "Estonia          See notes for:  Gross domestic product, curren...   \n",
       "Greece           See notes for:  Gross domestic product, curren...   \n",
       "Portugal         See notes for:  Gross domestic product, curren...   \n",
       "Slovenia         See notes for:  Gross domestic product, curren...   \n",
       "Spain            See notes for:  Gross domestic product, curren...   \n",
       "Korea            See notes for:  Gross domestic product, curren...   \n",
       "Italy            See notes for:  Gross domestic product, curren...   \n",
       "Japan            See notes for:  Gross domestic product, curren...   \n",
       "Israel           See notes for:  Gross domestic product, curren...   \n",
       "New Zealand      See notes for:  Gross domestic product, curren...   \n",
       "France           See notes for:  Gross domestic product, curren...   \n",
       "Belgium          See notes for:  Gross domestic product, curren...   \n",
       "Germany          See notes for:  Gross domestic product, curren...   \n",
       "Finland          See notes for:  Gross domestic product, curren...   \n",
       "Canada           See notes for:  Gross domestic product, curren...   \n",
       "Netherlands      See notes for:  Gross domestic product, curren...   \n",
       "Austria          See notes for:  Gross domestic product, curren...   \n",
       "United Kingdom   See notes for:  Gross domestic product, curren...   \n",
       "Sweden           See notes for:  Gross domestic product, curren...   \n",
       "Iceland          See notes for:  Gross domestic product, curren...   \n",
       "Australia        See notes for:  Gross domestic product, curren...   \n",
       "Ireland          See notes for:  Gross domestic product, curren...   \n",
       "Denmark          See notes for:  Gross domestic product, curren...   \n",
       "United States    See notes for:  Gross domestic product, curren...   \n",
       "Norway           See notes for:  Gross domestic product, curren...   \n",
       "Switzerland      See notes for:  Gross domestic product, curren...   \n",
       "Luxembourg       See notes for:  Gross domestic product, curren...   \n",
       "\n",
       "                      人均GDP  Estimates Start After  \n",
       "Country                                             \n",
       "Brazil             8669.998                 2014.0  \n",
       "Mexico             9009.280                 2015.0  \n",
       "Russia             9054.914                 2015.0  \n",
       "Turkey             9437.372                 2013.0  \n",
       "Hungary           12239.894                 2015.0  \n",
       "Poland            12495.334                 2014.0  \n",
       "Chile             13340.905                 2014.0  \n",
       "Slovak Republic   15991.736                 2015.0  \n",
       "Czech Republic    17256.918                 2015.0  \n",
       "Estonia           17288.083                 2014.0  \n",
       "Greece            18064.288                 2014.0  \n",
       "Portugal          19121.592                 2014.0  \n",
       "Slovenia          20732.482                 2015.0  \n",
       "Spain             25864.721                 2014.0  \n",
       "Korea             27195.197                 2014.0  \n",
       "Italy             29866.581                 2015.0  \n",
       "Japan             32485.545                 2015.0  \n",
       "Israel            35343.336                 2015.0  \n",
       "New Zealand       37044.891                 2015.0  \n",
       "France            37675.006                 2015.0  \n",
       "Belgium           40106.632                 2014.0  \n",
       "Germany           40996.511                 2014.0  \n",
       "Finland           41973.988                 2014.0  \n",
       "Canada            43331.961                 2015.0  \n",
       "Netherlands       43603.115                 2014.0  \n",
       "Austria           43724.031                 2015.0  \n",
       "United Kingdom    43770.688                 2015.0  \n",
       "Sweden            49866.266                 2014.0  \n",
       "Iceland           50854.583                 2014.0  \n",
       "Australia         50961.865                 2014.0  \n",
       "Ireland           51350.744                 2014.0  \n",
       "Denmark           52114.165                 2015.0  \n",
       "United States     55805.204                 2015.0  \n",
       "Norway            74822.106                 2015.0  \n",
       "Switzerland       80675.308                 2015.0  \n",
       "Luxembourg       101994.093                 2014.0  \n",
       "\n",
       "[36 rows x 30 columns]"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample_data = pd.merge(left=oecd_bli, right=gdp_per_capita, left_index=True, right_index=True)\n",
    "sample_data.sort_values(by=\"人均GDP\", inplace=True)\n",
    "sample_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "a73cf8f8-9e53-4a37-91b8-906d2e10fc8c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "人均GDP    25864.721\n",
       "生活满意度        6.500\n",
       "Name: Spain, dtype: float64"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample_data[[\"人均GDP\", \"生活满意度\"]].loc[\"Spain\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "30bd2325-58e1-4053-a221-97b826437c00",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>人均GDP</th>\n",
       "      <th>生活满意度</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Russia</th>\n",
       "      <td>9054.914</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Turkey</th>\n",
       "      <td>9437.372</td>\n",
       "      <td>5.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hungary</th>\n",
       "      <td>12239.894</td>\n",
       "      <td>4.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Poland</th>\n",
       "      <td>12495.334</td>\n",
       "      <td>5.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Slovak Republic</th>\n",
       "      <td>15991.736</td>\n",
       "      <td>6.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Estonia</th>\n",
       "      <td>17288.083</td>\n",
       "      <td>5.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Greece</th>\n",
       "      <td>18064.288</td>\n",
       "      <td>4.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Portugal</th>\n",
       "      <td>19121.592</td>\n",
       "      <td>5.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Slovenia</th>\n",
       "      <td>20732.482</td>\n",
       "      <td>5.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Spain</th>\n",
       "      <td>25864.721</td>\n",
       "      <td>6.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Korea</th>\n",
       "      <td>27195.197</td>\n",
       "      <td>5.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Italy</th>\n",
       "      <td>29866.581</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Japan</th>\n",
       "      <td>32485.545</td>\n",
       "      <td>5.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Israel</th>\n",
       "      <td>35343.336</td>\n",
       "      <td>7.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New Zealand</th>\n",
       "      <td>37044.891</td>\n",
       "      <td>7.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>France</th>\n",
       "      <td>37675.006</td>\n",
       "      <td>6.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Belgium</th>\n",
       "      <td>40106.632</td>\n",
       "      <td>6.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Germany</th>\n",
       "      <td>40996.511</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Finland</th>\n",
       "      <td>41973.988</td>\n",
       "      <td>7.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Canada</th>\n",
       "      <td>43331.961</td>\n",
       "      <td>7.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Netherlands</th>\n",
       "      <td>43603.115</td>\n",
       "      <td>7.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Austria</th>\n",
       "      <td>43724.031</td>\n",
       "      <td>6.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United Kingdom</th>\n",
       "      <td>43770.688</td>\n",
       "      <td>6.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sweden</th>\n",
       "      <td>49866.266</td>\n",
       "      <td>7.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Iceland</th>\n",
       "      <td>50854.583</td>\n",
       "      <td>7.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Australia</th>\n",
       "      <td>50961.865</td>\n",
       "      <td>7.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ireland</th>\n",
       "      <td>51350.744</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Denmark</th>\n",
       "      <td>52114.165</td>\n",
       "      <td>7.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United States</th>\n",
       "      <td>55805.204</td>\n",
       "      <td>7.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     人均GDP  生活满意度\n",
       "Country                          \n",
       "Russia            9054.914    6.0\n",
       "Turkey            9437.372    5.6\n",
       "Hungary          12239.894    4.9\n",
       "Poland           12495.334    5.8\n",
       "Slovak Republic  15991.736    6.1\n",
       "Estonia          17288.083    5.6\n",
       "Greece           18064.288    4.8\n",
       "Portugal         19121.592    5.1\n",
       "Slovenia         20732.482    5.7\n",
       "Spain            25864.721    6.5\n",
       "Korea            27195.197    5.8\n",
       "Italy            29866.581    6.0\n",
       "Japan            32485.545    5.9\n",
       "Israel           35343.336    7.4\n",
       "New Zealand      37044.891    7.3\n",
       "France           37675.006    6.5\n",
       "Belgium          40106.632    6.9\n",
       "Germany          40996.511    7.0\n",
       "Finland          41973.988    7.4\n",
       "Canada           43331.961    7.3\n",
       "Netherlands      43603.115    7.3\n",
       "Austria          43724.031    6.9\n",
       "United Kingdom   43770.688    6.8\n",
       "Sweden           49866.266    7.2\n",
       "Iceland          50854.583    7.5\n",
       "Australia        50961.865    7.3\n",
       "Ireland          51350.744    7.0\n",
       "Denmark          52114.165    7.5\n",
       "United States    55805.204    7.2"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#拿出几个数据\n",
    "# remove_indices = [i for i in range(0,36) if i % 3 == 0]\n",
    "remove_indices = [0, 1, 6, 8, 33, 34, 35]\n",
    "#差集\n",
    "keep_indices = list(set(range(36)) - set(remove_indices))\n",
    "\n",
    "sample_data_2 = sample_data[[\"人均GDP\", \"生活满意度\"]].iloc[keep_indices]\n",
    "sample_data_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "5ed460ee-4242-4282-99fc-27d74afc58d2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>人均GDP</th>\n",
       "      <th>生活满意度</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Brazil</th>\n",
       "      <td>8669.998</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mexico</th>\n",
       "      <td>9009.280</td>\n",
       "      <td>6.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chile</th>\n",
       "      <td>13340.905</td>\n",
       "      <td>6.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Czech Republic</th>\n",
       "      <td>17256.918</td>\n",
       "      <td>6.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Norway</th>\n",
       "      <td>74822.106</td>\n",
       "      <td>7.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Switzerland</th>\n",
       "      <td>80675.308</td>\n",
       "      <td>7.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Luxembourg</th>\n",
       "      <td>101994.093</td>\n",
       "      <td>6.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     人均GDP  生活满意度\n",
       "Country                          \n",
       "Brazil            8669.998    7.0\n",
       "Mexico            9009.280    6.7\n",
       "Chile            13340.905    6.7\n",
       "Czech Republic   17256.918    6.5\n",
       "Norway           74822.106    7.4\n",
       "Switzerland      80675.308    7.5\n",
       "Luxembourg      101994.093    6.9"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#查看删除的数据\n",
    "missing_data = sample_data[[\"人均GDP\", \"生活满意度\"]].iloc[remove_indices]\n",
    "missing_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "f87552c7",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-12T01:59:40.509231Z",
     "start_time": "2024-09-12T01:59:40.202288Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure money_happy_scatterplot\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sample_data_2.plot(kind='scatter', x=\"人均GDP\", y='生活满意度', figsize=(5,3))\n",
    "plt.axis([0, 60000, 0, 10])\n",
    "position_text = {\n",
    "    \"Hungary\": (5000, 1),\n",
    "    \"Korea\": (18000, 1.7),\n",
    "    \"France\": (29000, 2.4),\n",
    "    \"Australia\": (40000, 3.0),\n",
    "    \"United States\": (52000, 3.8),\n",
    "}\n",
    "for country, pos_text in position_text.items():\n",
    "    pos_data_x, pos_data_y = sample_data_2.loc[country]\n",
    "    if country == \"United States\":  country = \"美国\"\n",
    "    if country == \"Hungary\": country = \"匈牙利\"\n",
    "    if country == \"Korea\": country = \"韩国\" \n",
    "    if country == \"France\": country = \"法国\" \n",
    "    if country == \"Australia\": country = \"澳大利亚\" \n",
    "    plt.annotate(country, xy=(pos_data_x, pos_data_y), xytext=pos_text,\n",
    "            arrowprops=dict(facecolor='black', width=0.5, shrink=0.1, headwidth=5))\n",
    "    plt.plot(pos_data_x, pos_data_y, \"ro\")\n",
    "save_fig('money_happy_scatterplot')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "98c20e01-9155-484b-be63-cc8376717a22",
   "metadata": {},
   "source": [
    "从图表里分析可以看出，越有钱的人好像越幸福。<br>\n",
    "建模：一个关于生活满意度的线性模型，这里用到的应该是线性回归模型。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "8f733894",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure tweaking_model_params_plot\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "sample_data_2.plot(kind='scatter', x=\"人均GDP\", y='生活满意度', figsize=(5,3))\n",
    "plt.axis([0, 60000, 0, 10])\n",
    "X=np.linspace(0, 60000, 1000)\n",
    "\n",
    "#对西塔取一些值进行拟合调试\n",
    "#西塔0=0， 西塔1=2*10**-5\n",
    "plt.plot(X, 2*X/100000, \"r\")\n",
    "plt.text(40000, 2.7, r\"$\\theta_0 = 0$\", fontsize=14, color=\"r\")\n",
    "plt.text(40000, 1.8, r\"$\\theta_1 = 2 \\times 10^{-5}$\", fontsize=14, color=\"r\")\n",
    "\n",
    "#西塔0=8， 西塔1=-5*10**-5\n",
    "plt.plot(X, 8 - 5*X/100000, \"g\")\n",
    "plt.text(5000, 9.1, r\"$\\theta_0 = 8$\", fontsize=14, color=\"g\")\n",
    "plt.text(5000, 8.2, r\"$\\theta_1 = -5 \\times 10^{-5}$\", fontsize=14, color=\"g\")\n",
    "\n",
    "#西塔0=4， 西塔1=5*10**-5\n",
    "plt.plot(X, 4 + 5*X/100000, \"b\")\n",
    "plt.text(5000, 3.5, r\"$\\theta_0 = 4$\", fontsize=14, color=\"b\")\n",
    "plt.text(5000, 2.6, r\"$\\theta_1 = 5 \\times 10^{-5}$\", fontsize=14, color=\"b\")\n",
    "\n",
    "#西塔0=5， 西塔1=5*10**-5\n",
    "plt.plot(X, 5 + 5*X/100000, \"pink\")\n",
    "plt.text(45000, 8.1, r\"$\\theta_0 = 5$\", fontsize=14, color=\"pink\")\n",
    "plt.text(45000, 7.2, r\"$\\theta_1 = 5 \\times 10^{-5}$\", fontsize=14, color=\"pink\")\n",
    "\n",
    "save_fig('tweaking_model_params_plot')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "92aed09e-fb57-452e-bfd1-d0ff27b367c7",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import linear_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "77ed1a88-dc65-4b40-9aff-1123d7a2c234",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5.763029861307918, 2.317733704739607e-05)"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_lin1 = linear_model.LinearRegression()\n",
    "Xsample = np.c_[sample_data[\"人均GDP\"]]\n",
    "ysample = np.c_[sample_data[\"生活满意度\"]]\n",
    "model_lin1.fit(Xsample, ysample)\n",
    "t0, t1 = model_lin1.intercept_[0], model_lin1.coef_[0][0]\n",
    "t0, t1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "c72fe7fa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure best_fit_model_plot\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sample_data.plot(kind='scatter', x=\"人均GDP\", y='生活满意度', figsize=(5,3))\n",
    "plt.axis([0, 60000, 0, 10])\n",
    "X=np.linspace(0, 60000, 1000)\n",
    "plt.plot(X, t0 + t1*X, \"b\")\n",
    "plt.text(5000, 3.1, r\"$\\theta_0 = 5.76$\", fontsize=14, color=\"b\")\n",
    "plt.text(5000, 2.2, r\"$\\theta_1 = 2.31 \\times 10^{-5}$\", fontsize=14, color=\"b\")\n",
    "save_fig('best_fit_model_plot')\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {
    "effccd36-7b3c-4561-9853-bc0761b2af78.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "id": "dbc8979c-edf6-4a99-8bdb-24f311000bc3",
   "metadata": {},
   "source": [
    "![image.png](attachment:effccd36-7b3c-4561-9853-bc0761b2af78.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1fe7e081-b8e1-44f4-be88-929d06c2d6b1",
   "metadata": {},
   "source": [
    "经查询，中国2023年的人均GDP为12518美元，应用模型得到生活满意度"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "7b4ed61d-17bd-4e98-982f-eeecae41382c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6.053163766467222"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "China_pred_life_satisfaction = model_lin1.predict([[12518]])[0][0]\n",
    "China_pred_life_satisfaction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "e671d53e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure China_prediction_plot\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 可视化\n",
    "sample_data_2.plot(kind='scatter', x=\"人均GDP\", y='生活满意度', figsize=(5,3), s=1)\n",
    "X=np.linspace(0, 60000, 1000)\n",
    "plt.plot(X, t0 + t1*X, \"b\")\n",
    "plt.axis([0, 60000, 0, 10])\n",
    "plt.text(5000, 7.5, r\"$\\theta_0 = 5.76$\", fontsize=14, color=\"b\")\n",
    "plt.text(5000, 6.6, r\"$\\theta_1 = 2.31 \\times 10^{-5}$\", fontsize=14, color=\"b\")\n",
    "plt.plot([12518, 12518], [0, China_pred_life_satisfaction], \"r--\")\n",
    "plt.text(9500, 4.5, r\"Prediction = 6.05\", fontsize=14, color=\"b\")\n",
    "plt.plot(12518, China_pred_life_satisfaction, \"ro\")\n",
    "save_fig('China_prediction_plot')\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {
    "4a695628-4e84-4434-bd95-b2142851798d.png": {
     "image/png": 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    }
   },
   "cell_type": "markdown",
   "id": "d273151f-6465-4a03-8329-ce5e006826ae",
   "metadata": {},
   "source": [
    "![image.png](attachment:4a695628-4e84-4434-bd95-b2142851798d.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "c169f648",
   "metadata": {},
   "outputs": [],
   "source": [
    "#将上面建立的模型封装到函数里\n",
    "def prepare_country_stats(oecd_bli, gdp_per_capita):\n",
    "    oecd_bli = oecd_bli[oecd_bli[\"INEQUALITY\"]==\"TOT\"]\n",
    "    oecd_bli = oecd_bli.pivot(index=\"Country\", columns=\"Indicator\", values=\"Value\")\n",
    "    oecd_bli.rename(columns={'Life satisfaction':'生活满意度'}, inplace=True)\n",
    "    gdp_per_capita.rename(columns={\"2015\": \"人均GDP\"}, inplace=True)\n",
    "    gdp_per_capita.set_index(\"Country\", inplace=True)\n",
    "    full_country_stats = pd.merge(left=oecd_bli, right=gdp_per_capita,\n",
    "                                  left_index=True, right_index=True)\n",
    "    full_country_stats.sort_values(by=\"人均GDP\", inplace=True)\n",
    "    remove_indices = [0, 1, 6, 8, 33, 34, 35]\n",
    "    keep_indices = list(set(range(36)) - set(remove_indices))\n",
    "    return full_country_stats[[\"人均GDP\", '生活满意度']].iloc[keep_indices]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "ab1e756a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[5.24548521]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Code example\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import sklearn.linear_model\n",
    "\n",
    "# Load the data\n",
    "oecd_bli = pd.read_csv(datapath + \"oecd_bli_2015.csv\", thousands=',')\n",
    "gdp_per_capita = pd.read_csv(datapath + \"gdp_per_capita.csv\",thousands=',',delimiter='\\t',\n",
    "                             encoding='latin1', na_values=\"n/a\")\n",
    "\n",
    "# Prepare the data\n",
    "country_stats = prepare_country_stats(oecd_bli, gdp_per_capita)\n",
    "X = np.c_[country_stats[\"人均GDP\"]]\n",
    "y = np.c_[country_stats[\"生活满意度\"]]\n",
    "\n",
    "# Visualize the data\n",
    "country_stats.plot(kind='scatter', x=\"人均GDP\", y='生活满意度')\n",
    "\n",
    "# Select a linear model\n",
    "model = sklearn.linear_model.LinearRegression()\n",
    "\n",
    "# Train the model\n",
    "model.fit(X, y)\n",
    "\n",
    "# Make a prediction for China\n",
    "X_new = [[7990]]  # China' GDP per capita\n",
    "China_predicted_life_satisfaction = model.predict(X_new)\n",
    "print(China_predicted_life_satisfaction) # outputs [[5.24548521]]\n",
    "plt.plot(X_new, China_predicted_life_satisfaction, \"ro\")\n",
    "plt.annotate('中国', xy=(7990,China_predicted_life_satisfaction), xytext=(13000,5.1),\n",
    "             arrowprops=dict(facecolor='black', width=0.5, shrink=0.1, headwidth=5))\n",
    "\n",
    "#2015中国人均GDP估计\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "22b941e0-b74f-488f-a617-3e44e47678e2",
   "metadata": {},
   "source": [
    "上面得到的missing_data，原因是这几个国家是特别贫穷或者特别富裕的，生活满意度不像中等收入国家那样与金钱成正比，可能会更多地追求精神上的富足。<br>\n",
    "把这几项除去相当于做数据清理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "7aec07d6-9398-4073-8bcf-899414bb2c19",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>人均GDP</th>\n",
       "      <th>生活满意度</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Brazil</th>\n",
       "      <td>8669.998</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mexico</th>\n",
       "      <td>9009.280</td>\n",
       "      <td>6.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chile</th>\n",
       "      <td>13340.905</td>\n",
       "      <td>6.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Czech Republic</th>\n",
       "      <td>17256.918</td>\n",
       "      <td>6.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Norway</th>\n",
       "      <td>74822.106</td>\n",
       "      <td>7.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Switzerland</th>\n",
       "      <td>80675.308</td>\n",
       "      <td>7.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Luxembourg</th>\n",
       "      <td>101994.093</td>\n",
       "      <td>6.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     人均GDP  生活满意度\n",
       "Country                          \n",
       "Brazil            8669.998    7.0\n",
       "Mexico            9009.280    6.7\n",
       "Chile            13340.905    6.7\n",
       "Czech Republic   17256.918    6.5\n",
       "Norway           74822.106    7.4\n",
       "Switzerland      80675.308    7.5\n",
       "Luxembourg      101994.093    6.9"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "missing_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "edee0f7e",
   "metadata": {},
   "outputs": [],
   "source": [
    "position_text2 = {\n",
    "    \"Brazil\": (1000, 9.0),\n",
    "    \"Mexico\": (11000, 9.0),\n",
    "    \"Chile\": (25000, 9.0),\n",
    "    \"Czech Republic\": (35000, 9.0),\n",
    "    \"Norway\": (60000, 3),\n",
    "    \"Switzerland\": (72000, 3.0),\n",
    "    \"Luxembourg\": (90000, 3.0),\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "0851097e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure representative_training_data_scatterplot\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sample_data_2.plot(kind='scatter', x=\"人均GDP\", y='生活满意度', figsize=(8,3))\n",
    "plt.axis([0, 110000, 0, 10])\n",
    "\n",
    "for country, pos_text in position_text2.items():\n",
    "    pos_data_x, pos_data_y = missing_data.loc[country]\n",
    "    if country == \"Brazil\":  country = \"巴西\"\n",
    "    if country == \"Mexico\": country = \"墨西哥\"\n",
    "    if country == \"Chile\": country = \"智利\" \n",
    "    if country == \"Czech Republic\": country = \"捷克\" \n",
    "    if country == \"Norway\": country = \"挪威\" \n",
    "    if country == \"Switzerland\": country = \"瑞士\" \n",
    "    if country == \"Luxembourg\": country = \"卢森堡\" \n",
    "    plt.annotate(country, xy=(pos_data_x, pos_data_y), xytext=pos_text,\n",
    "            arrowprops=dict(facecolor='black', width=0.5, shrink=0.1, headwidth=5))\n",
    "    plt.plot(pos_data_x, pos_data_y, \"rs\")\n",
    "\n",
    "X=np.linspace(0, 110000, 1000)\n",
    "plt.plot(X, t0 + t1*X, \"b:\")\n",
    "\n",
    "lin_reg_full = linear_model.LinearRegression()\n",
    "Xfull = np.c_[sample_data_2[\"人均GDP\"]]\n",
    "yfull = np.c_[sample_data_2[\"生活满意度\"]]\n",
    "lin_reg_full.fit(Xfull, yfull)\n",
    "\n",
    "t0full, t1full = lin_reg_full.intercept_[0], lin_reg_full.coef_[0][0]\n",
    "X = np.linspace(0, 110000, 1000)\n",
    "plt.plot(X, t0full + t1full * X, \"k\")\n",
    "\n",
    "save_fig('representative_training_data_scatterplot')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "65a1080c-a542-48c8-a2b7-6d44c7428db1",
   "metadata": {},
   "source": [
    "过拟合的情况：模型不适用该场景"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "051a5b91",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure overfitting_model_plot\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sample_data.plot(kind='scatter', x=\"人均GDP\", y='生活满意度', figsize=(8,3))\n",
    "plt.axis([0, 110000, 0, 10])\n",
    "\n",
    "from sklearn import preprocessing\n",
    "from sklearn import pipeline\n",
    "\n",
    "#多项式回归模型\n",
    "poly = preprocessing.PolynomialFeatures(degree=30, include_bias=False)\n",
    "scaler = preprocessing.StandardScaler()\n",
    "lin_reg2 = linear_model.LinearRegression()\n",
    "\n",
    "pipeline_reg = pipeline.Pipeline([('poly', poly), ('scal', scaler), ('lin', lin_reg2)])\n",
    "pipeline_reg.fit(Xfull, yfull)\n",
    "curve = pipeline_reg.predict(X[:, np.newaxis])\n",
    "plt.plot(X, curve)\n",
    "plt.xlabel(\"GDP per capita (USD)\")\n",
    "save_fig('overfitting_model_plot')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ce1dc0c9-fcd9-4039-9c87-af600d7204bf",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
